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            "_view_module_version": "1.2.0",
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    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "id": "EtSdhnyTr5Uq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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 tensorflow as tf\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"df.csv\")"
      ],
      "metadata": {
        "id": "VlDXIzC7sUxX"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n = 5000"
      ],
      "metadata": {
        "id": "_zWqNJJHsWDb"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "5sEGRJucsXgG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('all_title.npy', all_title)"
      ],
      "metadata": {
        "id": "aep4nHnlMgIB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(all_q, all_des, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "RiNv-JdccuHG"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_tt, x_val, y_tt, y_val = train_test_split(x_train, y_train, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "xKJi5QAqcLAz"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#for first time\n",
        "model = SentenceTransformer('Madnesss/fine-tune-paraphrase-multilingual-mpnet-base-v2')"
      ],
      "metadata": {
        "id": "BTg_TAgc2Hal"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = SentenceTransformer(\"/content/gdrive/MyDrive/model/model_multi_tripet_2.pt\")"
      ],
      "metadata": {
        "id": "qX1D27NEawxC"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for cosine sim loss\n",
        "# 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))"
      ],
      "metadata": {
        "id": "ACc2h9KG3kmM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "em_des = model.encode(y_train, convert_to_tensor=True)\n",
        "em_q = model.encode(x_train, convert_to_tensor=True)\n",
        "cosine_scoreee = util.cos_sim(em_des, em_des)"
      ],
      "metadata": {
        "id": "FZcZpV4dpzdy"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#generate negative example using tripet loss\n",
        "train_examples = []\n",
        "n_sam = len(x_train)\n",
        "counter = 0\n",
        "\n",
        "for i in range(n_sam):\n",
        "  pivot_q = x_train[i]\n",
        "  all_idx = torch.topk(cosine_scoreee[i], 2).indices\n",
        "  if(all_idx[0]==i):\n",
        "    train_examples.append(InputExample(texts=[pivot_q, y_train[i], y_train[all_idx[1]]]))\n",
        "  else:\n",
        "    train_examples.append(InputExample(texts=[pivot_q, y_train[i], y_train[all_idx[0]]]))"
      ],
      "metadata": {
        "id": "lHTbjdjtTFDu"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = DataLoader(train_examples, batch_size=16)\n",
        "train_loss = losses.TripletLoss(model=model)\n",
        "\n",
        "model.fit(train_objectives=[(train_dataloader, train_loss)],\n",
        "          epochs= 3, warmup_steps=100, show_progress_bar=True)"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 31,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/199 [00:00<?, ?it/s]"
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        {
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          "data": {
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              "Iteration:   0%|          | 0/199 [00:00<?, ?it/s]"
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        {
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          "data": {
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              "Iteration:   0%|          | 0/199 [00:00<?, ?it/s]"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"./model_multi_tripet_2.pt\")"
      ],
      "metadata": {
        "id": "1p24R5H951Cs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r /content/model_multi_tripet_2.zip /content/model_multi_tripet_2.pt"
      ],
      "metadata": {
        "id": "fmvTWSRCq4wG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59a0410e-4264-4f37-8645-8a49f7bc6952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/model_multi_tripet_2.pt/ (stored 0%)\n",
            "  adding: content/model_multi_tripet_2.pt/sentence_bert_config.json (deflated 4%)\n",
            "  adding: content/model_multi_tripet_2.pt/1_Pooling/ (stored 0%)\n",
            "  adding: content/model_multi_tripet_2.pt/1_Pooling/config.json (deflated 47%)\n",
            "  adding: content/model_multi_tripet_2.pt/config_sentence_transformers.json (deflated 26%)\n",
            "  adding: content/model_multi_tripet_2.pt/sentencepiece.bpe.model (deflated 49%)\n",
            "  adding: content/model_multi_tripet_2.pt/README.md (deflated 58%)\n",
            "  adding: content/model_multi_tripet_2.pt/special_tokens_map.json (deflated 52%)\n",
            "  adding: content/model_multi_tripet_2.pt/tokenizer_config.json (deflated 49%)\n",
            "  adding: content/model_multi_tripet_2.pt/modules.json (deflated 53%)\n",
            "  adding: content/model_multi_tripet_2.pt/pytorch_model.bin (deflated 9%)\n",
            "  adding: content/model_multi_tripet_2.pt/config.json (deflated 48%)\n",
            "  adding: content/model_multi_tripet_2.pt/tokenizer.json (deflated 76%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = 'Madnesss/fine-tune-paraphrase-multilingual-mpnet-base-v2'\n",
        "model = SentenceTransformer(path)"
      ],
      "metadata": {
        "id": "iFljWwtD0lJk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_q = x_test"
      ],
      "metadata": {
        "id": "W6sR_9ni_toF"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings_des = model.encode(all_des, convert_to_tensor=True)"
      ],
      "metadata": {
        "id": "_5oQzXCu_3s2"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "PI4zFqYo_vOl"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(embeddings_des[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xEfGN7h2ATpp",
        "outputId": "17d3a53d-82a1-48ee-8983-8b518494d95e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(all_val_q)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3TpmZG_LI85",
        "outputId": "6d5d81fd-f572-47d2-f19b-d579659f045c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1060"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "QgaBujI__min"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"top1 acc :\", acc/len(all_val_q))"
      ],
      "metadata": {
        "id": "-LA_I9bY_q3o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "421fb83c-6885-4b7b-aa64-883a9835ac21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "top1 acc : 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"top3 acc :\", acc/len(all_val_q))"
      ],
      "metadata": {
        "id": "Amu8c2CNXK-s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ae1ace7-0e42-482d-be48-6557d033dd89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "top3 acc : 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"top5 acc :\", acc/len(all_val_q))"
      ],
      "metadata": {
        "id": "zS7ykzjbXnv9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19739255-0f83-43f2-c604-39bc43aa6ccb"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "top5 acc : 0.6056603773584905\n"
          ]
        }
      ]
    }
  ]
}