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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 유사도 챗봇 성능 향상 기법\n",
        "🤔 키워드는 잡아내면서 질문의 맥락도 캐치하고 싶다<br>\n",
        "💡 BM25 + Faiss 앙상블 모델을 결합하여 유사도를 측정해 보자!"
      ],
      "metadata": {
        "id": "CWqRJ-Qift-u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 데이터 불러와서 리스트 생성"
      ],
      "metadata": {
        "id": "40pPMPZVgPeE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain langchain_community kiwipiepy rank_bm25 sentence_transformers faiss-gpu datasets transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0gH4j5CHnEv",
        "outputId": "a0cbbc5a-ef3b-419a-d322-ea0ade1a22c0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (0.2.16)\n",
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            "Requirement already satisfied: tenacity!=8.4.0,<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (8.5.0)\n",
            "Requirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (0.6.7)\n",
            "Requirement already satisfied: kiwipiepy-model<0.19,>=0.18 in /usr/local/lib/python3.10/dist-packages (from kiwipiepy) (0.18.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kiwipiepy) (4.66.5)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (2.4.0+cu121)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.3.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.13.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.24.6)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (9.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n",
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            "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
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            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Excel 파일 경로 지정\n",
        "file_path = '중학사회(취합).xlsx'\n",
        "\n",
        "# Excel 파일 읽기\n",
        "df = pd.read_excel(file_path, engine='openpyxl')\n",
        "\n",
        "# 질문과 답변 리스트 생성\n",
        "questions = df['SENT_CONTENT'].tolist()\n",
        "int_sentences = df['INT_SENTENCE'].tolist()\n",
        "int_responses = df['INT_RESPONSE'].tolist()\n",
        "\n",
        "print(\"총 질문 개수:\", len(questions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQWIjxIFH8BB",
        "outputId": "59279fe5-fe68-406e-f9e3-dd31d9b55e82"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "총 질문 개수: 4867\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. BM25 단독"
      ],
      "metadata": {
        "id": "MCecPkrfgTth"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from rank_bm25 import BM25Okapi\n",
        "from langchain_community.retrievers import BM25Retriever\n",
        "from kiwipiepy import Kiwi\n",
        "from langchain.schema import Document\n",
        "\n",
        "# Kiwi 형태소 분석기 설정\n",
        "kiwi = Kiwi()\n",
        "\n",
        "# 형태소 분석을 통해 문서를 토큰화하는 함수\n",
        "def kiwi_tokenize(text):\n",
        "    return [token.form for token in kiwi.tokenize(text)]\n",
        "\n",
        "# 문서와 쿼리를 토큰화\n",
        "tokenized_questions = [kiwi_tokenize(question) for question in questions]\n",
        "\n",
        "# BM25 모델 생성\n",
        "bm25 = BM25Okapi(tokenized_questions)\n",
        "\n",
        "# 사용자 질문 입력\n",
        "query = \"우리나라 정부 형태가 뭐야?\"\n",
        "\n",
        "documents = [Document(page_content=question, metadata={'index': i}) for i, question in enumerate(questions)]\n",
        "\n",
        "# BM25 모델 설정\n",
        "kiwi_bm25 = BM25Retriever.from_documents(documents, preprocess_func=kiwi_tokenize)\n",
        "\n",
        "retriever = kiwi_bm25\n",
        "\n",
        "# 검색 수행\n",
        "results = retriever.invoke(query)\n",
        "\n",
        "# 중복되지 않은 int_sentence를 저장할 set\n",
        "unique_sentences = set()\n",
        "\n",
        "# 상위 3개의 중복되지 않은 int_sentence를 찾는 리스트\n",
        "top_results = []\n",
        "\n",
        "# 결과에서 중복되지 않은 int_sentence만 저장\n",
        "for result in results:\n",
        "    index = result.metadata['index']  # 검색된 결과의 인덱스\n",
        "    if int_sentences[index] not in unique_sentences:\n",
        "        unique_sentences.add(int_sentences[index])  # 중복 제거\n",
        "        top_results.append(result)\n",
        "    if len(top_results) == 3:  # 3개의 고유한 int_sentence가 채워지면 종료\n",
        "        break\n",
        "\n",
        "# 결과 출력\n",
        "print(\"사용자 질문:\", query)\n",
        "print(\"\\n상위 3개 결과:\")\n",
        "\n",
        "for result in top_results:\n",
        "    index = result.metadata['index']\n",
        "\n",
        "    print(f\"질문: {int_sentences[index]}\")\n",
        "    print(f\"답변: {int_responses[index]}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODE4LEHqa_9X",
        "outputId": "efd1b2ed-8f4e-44fb-c1f2-ff034af28362"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "사용자 질문: 우리나라 정부 형태가 뭐야?\n",
            "\n",
            "상위 3개 결과:\n",
            "질문: 우리나라의 정부 형태에 대해 알려주세요.\n",
            "답변: 우리나라의 정부 형태는 의원내각제적 요소를 가미한 대통령제 입니다.\n",
            "\n",
            "질문: 총재 정부란 무엇인가요?\n",
            "답변: 총재 정부는 프랑스 혁명 당시 로베스피에르의 가혹한 공포 정치에 반발하여 일어난 정부로 5명의 총재가 행정을 담당하였습니다.\n",
            "\n",
            "질문: 우리나라 대통령제에서의 의원내각제적 요소는 무엇인가요?\n",
            "답변: 1. 국무총리제, 2. 국회의원의 장관직 겸직 가능, 3. 행정부의 법률안 제출권입니다.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Faiss 단독"
      ],
      "metadata": {
        "id": "CGiROLnLgd4H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from langchain.retrievers import EnsembleRetriever\n",
        "from langchain_community.retrievers import BM25Retriever\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.schema import Document\n",
        "\n",
        "from kiwipiepy import Kiwi\n",
        "\n",
        "# Kiwi 형태소 분석기 설정\n",
        "kiwi = Kiwi()\n",
        "\n",
        "# 형태소 분석을 통해 문서를 토큰화하는 함수\n",
        "def kiwi_tokenize(text):\n",
        "    return [token.form for token in kiwi.tokenize(text)]\n",
        "\n",
        "# 문서와 쿼리를 토큰화\n",
        "tokenized_questions = [kiwi_tokenize(question) for question in questions]\n",
        "\n",
        "# 사용자 질문 입력\n",
        "query = \"우리나라 정부 형태가 뭐야?\"\n",
        "\n",
        "documents = [Document(page_content=question, metadata={'index': i}) for i, question in enumerate(questions)]\n",
        "\n",
        "# FAISS 설정\n",
        "hf_model_name = \"intfloat/multilingual-e5-large\"  # contextualized embedding model 사용\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=hf_model_name)\n",
        "faiss = FAISS.from_documents(documents, hf_embeddings).as_retriever()\n",
        "\n",
        "retriever = faiss\n",
        "\n",
        "# 검색 수행\n",
        "results = retriever.invoke(query)\n",
        "\n",
        "# 중복되지 않은 int_sentence를 저장할 set\n",
        "unique_sentences = set()\n",
        "\n",
        "# 상위 3개의 중복되지 않은 int_sentence를 찾는 리스트\n",
        "top_results = []\n",
        "\n",
        "# 결과에서 중복되지 않은 int_sentence만 저장\n",
        "for result in results:\n",
        "    index = result.metadata['index']  # 검색된 결과의 인덱스\n",
        "    if int_sentences[index] not in unique_sentences:\n",
        "        unique_sentences.add(int_sentences[index])  # 중복 제거\n",
        "        top_results.append(result)\n",
        "    if len(top_results) == 3:  # 3개의 고유한 int_sentence가 채워지면 종료\n",
        "        break\n",
        "\n",
        "# 결과 출력\n",
        "print(\"사용자 질문:\", query)\n",
        "print(\"\\n상위 3개 결과:\")\n",
        "\n",
        "for result in top_results:\n",
        "    index = result.metadata['index']\n",
        "\n",
        "    print(f\"질문: {int_sentences[index]}\")\n",
        "    print(f\"답변: {int_responses[index]}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667,
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            "c3581d2fb7604489b5074a6f2c22dd28",
            "6534534b44014b7b9dda74328bbe8ca8",
            "89b3b77a8e85481791637cec75c5a260",
            "3e614add1cc8445fb465a2293d777b28",
            "212178d7dc824b0aaa82e110fb94c14f",
            "892df1ce31d54c318356b2ccc7c342a1",
            "f9f554e2786c4e66b05b0e4ac215d4e9",
            "e7aa7654a43741359555402faac0af85",
            "860c31c8253f493188fa73a4ab3f3db7",
            "56031ffc199d48729303b8ab83e2bc77",
            "82371be2788747d19295e986491ce7ba",
            "a873d9bdd6214a2b9bcb2fd793319871",
            "245b5090c3524ca9abcb35d0f6dfbe95"
          ]
        },
        "id": "8u6JVPZCdy1e",
        "outputId": "6e4d1bee-eb17-4e31-ff7c-1643983c525e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-59868297845c>:33: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
            "  hf_embeddings = HuggingFaceEmbeddings(model_name=hf_model_name)\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
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        {
          "output_type": "display_data",
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          "metadata": {}
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        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "사용자 질문: 우리나라 정부 형태가 뭐야?\n",
            "\n",
            "상위 3개 결과:\n",
            "질문: 우리나라의 정부 형태에 대해 알려주세요.\n",
            "답변: 우리나라의 정부 형태는 의원내각제적 요소를 가미한 대통령제 입니다.\n",
            "\n",
            "질문: 우리나라 대통령제에서의 의원내각제적 요소는 무엇인가요?\n",
            "답변: 1. 국무총리제, 2. 국회의원의 장관직 겸직 가능, 3. 행정부의 법률안 제출권입니다.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. 앙상블 모델 (1)"
      ],
      "metadata": {
        "id": "yIF83zZ4ghdQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 사용자 쿼리-데이터셋 질문 유사도 검색"
      ],
      "metadata": {
        "id": "wWrgdFEGLHPy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from langchain.retrievers import EnsembleRetriever\n",
        "from langchain_community.retrievers import BM25Retriever\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.schema import Document\n",
        "\n",
        "from kiwipiepy import Kiwi\n",
        "\n",
        "file_path = '중학사회(취합).xlsx'\n",
        "df = pd.read_excel(file_path, engine='openpyxl')\n",
        "\n",
        "# 질문과 답변 리스트 생성\n",
        "questions = df['SENT_CONTENT'].tolist()\n",
        "int_sentences = df['INT_SENTENCE'].tolist()\n",
        "int_responses = df['INT_RESPONSE'].tolist()\n",
        "\n",
        "# Kiwi 형태소 분석기 설정\n",
        "kiwi = Kiwi()\n",
        "\n",
        "# 형태소 분석을 통해 문서를 토큰화하는 함수\n",
        "def kiwi_tokenize(text):\n",
        "    return [token.form for token in kiwi.tokenize(text)]\n",
        "\n",
        "# 문서와 쿼리를 토큰화\n",
        "tokenized_questions = [kiwi_tokenize(question) for question in questions]\n",
        "\n",
        "documents = [Document(page_content=question, metadata={'index': i}) for i, question in enumerate(questions)]\n",
        "\n",
        "# BM25 모델 설정\n",
        "kiwi_bm25 = BM25Retriever.from_documents(documents, preprocess_func=kiwi_tokenize)\n",
        "\n",
        "# FAISS 설정\n",
        "hf_model_name = \"intfloat/multilingual-e5-large\"  # contextualized embedding model 사용\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=hf_model_name)\n",
        "faiss = FAISS.from_documents(documents, hf_embeddings).as_retriever()\n",
        "\n",
        "# EnsembleRetriever에서 검색 수행\n",
        "retriever = EnsembleRetriever(\n",
        "    retrievers=[kiwi_bm25, faiss],\n",
        "    weights=[0.3, 0.7],\n",
        "    search_type=\"mmr\",\n",
        ")"
      ],
      "metadata": {
        "id": "_35dR-Zmn7TX"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"우리나라 정부 형태가 뭐야?\"\n",
        "\n",
        "# 검색 수행\n",
        "results = retriever.invoke(query)\n",
        "\n",
        "# 중복되지 않은 int_sentence를 저장할 set\n",
        "unique_sentences = set()\n",
        "\n",
        "# 상위 3개의 중복되지 않은 int_sentence를 찾는 리스트\n",
        "top_results = []\n",
        "\n",
        "# 결과에서 중복되지 않은 int_sentence만 저장\n",
        "for result in results:\n",
        "    index = result.metadata['index']  # 검색된 결과의 인덱스\n",
        "    if int_sentences[index] not in unique_sentences:\n",
        "        unique_sentences.add(int_sentences[index])  # 중복 제거\n",
        "        top_results.append(result)\n",
        "    if len(top_results) == 3:  # 3개의 고유한 int_sentence가 채워지면 종료\n",
        "        break\n",
        "\n",
        "# 결과 출력\n",
        "print(\"사용자 질문:\", query)\n",
        "print(\"\\n상위 3개 결과:\")\n",
        "\n",
        "for result in top_results:\n",
        "    index = result.metadata['index']\n",
        "    print(f\"질문: {int_sentences[index]}\")\n",
        "    print(f\"답변: {int_responses[index]}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skre9eydqi3A",
        "outputId": "cf4f6ac2-37e5-4ec9-a067-bc57fef81c1f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "사용자 질문: 우리나라 정부 형태가 뭐야?\n",
            "\n",
            "상위 3개 결과:\n",
            "질문: 우리나라의 정부 형태에 대해 알려주세요.\n",
            "답변: 우리나라의 정부 형태는 의원내각제적 요소를 가미한 대통령제 입니다.\n",
            "\n",
            "질문: 우리나라 대통령제에서의 의원내각제적 요소는 무엇인가요?\n",
            "답변: 1. 국무총리제, 2. 국회의원의 장관직 겸직 가능, 3. 행정부의 법률안 제출권입니다.\n",
            "\n",
            "질문: 총재 정부란 무엇인가요?\n",
            "답변: 총재 정부는 프랑스 혁명 당시 로베스피에르의 가혹한 공포 정치에 반발하여 일어난 정부로 5명의 총재가 행정을 담당하였습니다.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 사용자 쿼리-데이터셋 답변 유사도 검색"
      ],
      "metadata": {
        "id": "aaNogYPvLLQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from langchain.retrievers import EnsembleRetriever\n",
        "from langchain_community.retrievers import BM25Retriever\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.schema import Document\n",
        "\n",
        "from kiwipiepy import Kiwi\n",
        "\n",
        "file_path = '중학사회(취합).xlsx'\n",
        "df = pd.read_excel(file_path, engine='openpyxl')\n",
        "\n",
        "# 질문과 답변 리스트 생성\n",
        "questions = df['SENT_CONTENT'].tolist()\n",
        "int_sentences = df['INT_SENTENCE'].tolist()\n",
        "int_responses = df['INT_RESPONSE'].tolist()\n",
        "\n",
        "# Kiwi 형태소 분석기 설정\n",
        "kiwi = Kiwi()\n",
        "\n",
        "# 형태소 분석을 통해 문서를 토큰화하는 함수\n",
        "def kiwi_tokenize(text):\n",
        "    return [token.form for token in kiwi.tokenize(text)]\n",
        "\n",
        "# 문서와 쿼리를 토큰화\n",
        "tokenized_int_responses = [kiwi_tokenize(response) for response in int_responses]\n",
        "\n",
        "documents = [Document(page_content=response, metadata={'index': i}) for i, response in enumerate(int_responses)]\n",
        "\n",
        "# BM25 모델 설정\n",
        "kiwi_bm25 = BM25Retriever.from_documents(documents, preprocess_func=kiwi_tokenize)\n",
        "\n",
        "# FAISS 설정\n",
        "hf_model_name = \"intfloat/multilingual-e5-large\"  # contextualized embedding model 사용\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=hf_model_name)\n",
        "faiss = FAISS.from_documents(documents, hf_embeddings).as_retriever()\n",
        "\n",
        "# EnsembleRetriever에서 검색 수행\n",
        "retriever = EnsembleRetriever(\n",
        "    retrievers=[kiwi_bm25, faiss],\n",
        "    weights=[0.3, 0.7],\n",
        "    search_type=\"mmr\",\n",
        ")"
      ],
      "metadata": {
        "id": "MVIv0amsKeyW"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"우리나라 정부 형태가 뭐야?\"\n",
        "\n",
        "# 검색 수행\n",
        "results = retriever.invoke(query)\n",
        "\n",
        "# 중복되지 않은 int_sentence를 저장할 set\n",
        "unique_sentences = set()\n",
        "\n",
        "# 상위 3개의 중복되지 않은 int_sentence를 찾는 리스트\n",
        "top_results = []\n",
        "\n",
        "# 결과에서 중복되지 않은 int_sentence만 저장\n",
        "for result in results:\n",
        "    index = result.metadata['index']  # 검색된 결과의 인덱스\n",
        "    if int_sentences[index] not in unique_sentences:\n",
        "        unique_sentences.add(int_sentences[index])  # 중복 제거\n",
        "        top_results.append(result)\n",
        "    if len(top_results) == 3:  # 3개의 고유한 int_sentence가 채워지면 종료\n",
        "        break\n",
        "\n",
        "# 결과 출력\n",
        "print(\"사용자 질문:\", query)\n",
        "print(\"\\n상위 3개 결과:\")\n",
        "\n",
        "for result in top_results:\n",
        "    index = result.metadata['index']\n",
        "    print(f\"질문: {int_sentences[index]}\")\n",
        "    print(f\"답변: {int_responses[index]}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_SeJIvPKk_3",
        "outputId": "c8951aa0-4a0d-4e71-8d98-69739a2edc84"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "사용자 질문: 우리나라 정부 형태가 뭐야?\n",
            "\n",
            "상위 3개 결과:\n",
            "질문: 우리나라의 정부 형태에 대해 알려주세요.\n",
            "답변: 우리나라의 정부 형태는 의원내각제적 요소를 가미한 대통령제 입니다.\n",
            "\n",
            "질문: 대통령제와 의원 내각제의 차이점이 무엇인가요?\n",
            "답변: 대통령제는 입법부와 행정부가 엄격하게 분리된 정부 형태입니다.\n",
            "의원 내각제는 입법부와 행정부의 권한이 융합된 정부 형태입니다.\n",
            "대통령제와 의원 내각제를 구분하는 방법은 정부와 의회의 융합 정도를 보면 됩니다.\n",
            "정부와 의회가 철저하게 구분되어 있으면 대통령제, 정부와 의회가 융합되어 있으면 의원 내각제입니다.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. 앙상블 모델 (2)"
      ],
      "metadata": {
        "id": "ml0bfPvtavap"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 데이터셋의 답변 내용에 따른 카테고리 추가"
      ],
      "metadata": {
        "id": "-fzpyaFMa0hZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from datasets import Dataset\n",
        "from transformers import pipeline\n",
        "\n",
        "# 파이프라인 설정 (GPU 사용)\n",
        "pipe = pipeline(\"zero-shot-classification\", model=\"MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7\", device=0)\n",
        "\n",
        "# JSON 파일 경로 지정\n",
        "file_path = '중학사회(취합).xlsx'\n",
        "df = pd.read_excel(file_path, engine='openpyxl')\n",
        "\n",
        "# pandas DataFrame을 Hugging Face Dataset으로 변환\n",
        "dataset = Dataset.from_pandas(df)\n",
        "\n",
        "# 카테고리 후보들\n",
        "candidate_labels = [\"정치\", \"경제\", \"사회\", \"문화\"]\n",
        "\n",
        "# 배치 처리로 카테고리 할당\n",
        "def categorize_batch(examples):\n",
        "    result = pipe(examples['INT_RESPONSE'], candidate_labels)\n",
        "    return {\"CATEGORY\": [res['labels'][0] for res in result]}  # 각 입력에 대해 가장 높은 점수의 카테고리 선택\n",
        "\n",
        "# dataset에 파이프라인 적용\n",
        "dataset = dataset.map(categorize_batch, batched=True)"
      ],
      "metadata": {
        "id": "LSzxxHc5Z-TJ",
        "colab": {
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        "outputId": "02206695-8669-4b70-b76c-1b55b2f1a11c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:  26%|##6       | 147M/558M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9acf0a244da4845869640c8db495e39"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/467 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d72f70f2c43444ca486a61e1f274551"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spm.model:   0%|          | 0.00/4.31M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c250500295049e0b8cd3e0a385a1530"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/16.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e40efb3c3ff4f3ea890b5620cd024a9"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/23.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aeea315c8fed4f5db4567010a6a80017"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/173 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5715a185b3147c8907143eab80a8fd4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4867 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0da864c2c9d641ce8196a1b8096d5e45"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-155cd4020b8a>\u001b[0m in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# dataset에 파이프라인 적용\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcategorize_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\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[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    600\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Dataset\"\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    603\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    565\u001b[0m         }\n\u001b[1;32m    566\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 567\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    568\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0;31m# re-apply format to the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3165\u001b[0m                     \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"Map\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3166\u001b[0m                 ) as pbar:\n\u001b[0;32m-> 3167\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_single\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdataset_kwargs\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   3168\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3169\u001b[0m                             \u001b[0mshards_done\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3556\u001b[0m                         )  # Something simpler?\n\u001b[1;32m   3557\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3558\u001b[0;31m                             batch = apply_function_on_filtered_inputs(\n\u001b[0m\u001b[1;32m   3559\u001b[0m                                 \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3560\u001b[0m                                 \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mapply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   3425\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mwith_rank\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3426\u001b[0m                 \u001b[0madditional_args\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrank\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-> 3427\u001b[0;31m             \u001b[0mprocessed_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0madditional_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfn_kwargs\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   3428\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessed_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyDict\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   3429\u001b[0m                 processed_inputs = {\n",
            "\u001b[0;32m<ipython-input-12-155cd4020b8a>\u001b[0m in \u001b[0;36mcategorize_batch\u001b[0;34m(examples)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# 배치 처리로 카테고리 할당\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcategorize_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\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---> 20\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'INT_RESPONSE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcandidate_labels\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     21\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"CATEGORY\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'labels'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m  \u001b[0;31m# 각 입력에 대해 가장 높은 점수의 카테고리 선택\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/zero_shot_classification.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, sequences, *args, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Unable to understand extra arguments {args}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpreprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcandidate_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhypothesis_template\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"This example is {}.\"\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/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1236\u001b[0m                     \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreprocess_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforward_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostprocess_params\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1237\u001b[0m                 )\n\u001b[0;32m-> 1238\u001b[0;31m                 \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_iterator\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   1239\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1240\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/pt_utils.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;31m# We're out of items within a batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterator\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    125\u001b[0m         \u001b[0mprocessed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0;31m# We now have a batch of \"inferred things\".\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/pt_utils.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_last\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m             \u001b[0mprocessed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\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    270\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloader_batch_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\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/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1162\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0minference_context\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   1163\u001b[0m                     \u001b[0mmodel_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_tensor_on_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1164\u001b[0;31m                     \u001b[0mmodel_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mforward_params\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   1165\u001b[0m                     \u001b[0mmodel_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_tensor_on_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cpu\"\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   1166\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/zero_shot_classification.py\u001b[0m in \u001b[0;36m_forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    227\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"use_cache\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\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    228\u001b[0m             \u001b[0mmodel_inputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"use_cache\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\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    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         model_outputs = {\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/deberta_v2/modeling_deberta_v2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1295\u001b[0m         \u001b[0mreturn_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_return_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1297\u001b[0;31m         outputs = self.deberta(\n\u001b[0m\u001b[1;32m   1298\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1299\u001b[0m             \u001b[0mtoken_type_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken_type_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/deberta_v2/modeling_deberta_v2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1061\u001b[0m         )\n\u001b[1;32m   1062\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m         encoder_outputs = self.encoder(\n\u001b[0m\u001b[1;32m   1064\u001b[0m             \u001b[0membedding_output\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/deberta_v2/modeling_deberta_v2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, output_hidden_states, output_attentions, query_states, relative_pos, return_dict)\u001b[0m\n\u001b[1;32m    505\u001b[0m                 )\n\u001b[1;32m    506\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m                 output_states = layer_module(\n\u001b[0m\u001b[1;32m    508\u001b[0m                     \u001b[0mnext_kv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m                     \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/deberta_v2/modeling_deberta_v2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, query_states, relative_pos, rel_embeddings, output_attentions)\u001b[0m\n\u001b[1;32m    353\u001b[0m         \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     ):\n\u001b[0;32m--> 355\u001b[0;31m         attention_output = self.attention(\n\u001b[0m\u001b[1;32m    356\u001b[0m             \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/deberta_v2/modeling_deberta_v2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions, query_states, relative_pos, rel_embeddings)\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0mrel_embeddings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m     ):\n\u001b[0;32m--> 286\u001b[0;31m         self_output = self.self(\n\u001b[0m\u001b[1;32m    287\u001b[0m             \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/deberta_v2/modeling_deberta_v2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions, query_states, relative_pos, rel_embeddings)\u001b[0m\n\u001b[1;32m    698\u001b[0m             \u001b[0mquery_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    699\u001b[0m         \u001b[0mquery_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose_for_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_attention_heads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 700\u001b[0;31m         \u001b[0mkey_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose_for_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_attention_heads\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    701\u001b[0m         \u001b[0mvalue_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose_for_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_attention_heads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/deberta_v2/modeling_deberta_v2.py\u001b[0m in \u001b[0;36mtranspose_for_scores\u001b[0;34m(self, x, attention_heads)\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0mnew_x_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\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[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mattention_heads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_x_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 657\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\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    658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    659\u001b[0m     def forward(\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결과를 DataFrame으로 변환 후 엑셀로 저장\n",
        "df_result = dataset.to_pandas()\n",
        "df_result.to_excel('./중학사회(취합)_카테고리 추가.xlsx', index=False, engine='openpyxl')"
      ],
      "metadata": {
        "id": "nbGTgfQDaENN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 사용자 쿼리와 동일한 카테고리의 답변을 가진 질의응답 대상으로 검색 수행"
      ],
      "metadata": {
        "id": "-NkH6qzna79Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 사용자 쿼리-데이터셋 질문 유사도 검색"
      ],
      "metadata": {
        "id": "ys1O_4MiNvVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "from langchain_core.documents import Document\n",
        "\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from langchain.retrievers import EnsembleRetriever\n",
        "from langchain_community.retrievers import BM25Retriever\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.schema import Document\n",
        "\n",
        "from kiwipiepy import Kiwi\n",
        "\n",
        "file_path = '중학사회(취합)_카테고리 추가.xlsx'\n",
        "df = pd.read_excel(file_path, engine='openpyxl')\n",
        "\n",
        "# 질문과 답변 리스트 생성\n",
        "questions = df['SENT_CONTENT'].tolist()\n",
        "int_sentences = df['INT_SENTENCE'].tolist()\n",
        "int_responses = df['INT_RESPONSE'].tolist()\n",
        "\n",
        "# Kiwi 형태소 분석기 설정\n",
        "kiwi = Kiwi()\n",
        "\n",
        "# 형태소 분석을 통해 문서를 토큰화하는 함수\n",
        "def kiwi_tokenize(text):\n",
        "    return [token.form for token in kiwi.tokenize(text)]\n",
        "\n",
        "# 문서와 쿼리를 토큰화\n",
        "tokenized_questions = [kiwi_tokenize(question) for question in questions]\n",
        "\n",
        "# 사용자 질문에 대한 카테고리 결정\n",
        "query = \"우리나라 정부 형태가 뭐야?\"\n",
        "category = '정치'\n",
        "\n",
        "# 특정 카테고리에 해당하는 문서만 필터링\n",
        "filtered_documents = []\n",
        "for i, question in enumerate(questions):\n",
        "    doc_category = df.loc[i, 'CATEGORY']\n",
        "    if doc_category == category:\n",
        "        filtered_documents.append(Document(page_content=question, metadata={'index': i, 'category': doc_category}))\n",
        "\n",
        "# BM25 모델 설정\n",
        "kiwi_bm25 = BM25Retriever.from_documents(filtered_documents, preprocess_func=kiwi_tokenize)\n",
        "\n",
        "# FAISS 설정\n",
        "hf_model_name = \"intfloat/multilingual-e5-large\"  # contextualized embedding model 사용\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=hf_model_name)\n",
        "faiss = FAISS.from_documents(filtered_documents, hf_embeddings).as_retriever()"
      ],
      "metadata": {
        "id": "YZ2LgZvNaB30"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# EnsembleRetriever에서 검색 수행\n",
        "retriever = EnsembleRetriever(\n",
        "    retrievers=[kiwi_bm25, faiss],\n",
        "    weights=[0.3, 0.7],\n",
        "    search_type=\"mmr\",\n",
        ")\n",
        "\n",
        "# 검색 수행\n",
        "results = retriever.invoke(query)\n",
        "\n",
        "# 중복되지 않은 int_sentence를 저장할 set\n",
        "unique_sentences = set()\n",
        "\n",
        "# 상위 3개의 중복되지 않은 int_sentence를 찾는 리스트\n",
        "top_results = []\n",
        "\n",
        "# 결과에서 중복되지 않은 int_sentence만 저장\n",
        "for result in results:\n",
        "    index = result.metadata['index']  # 검색된 결과의 인덱스\n",
        "    if int_sentences[index] not in unique_sentences:\n",
        "        unique_sentences.add(int_sentences[index])  # 중복 제거\n",
        "        top_results.append(result)\n",
        "    if len(top_results) == 3:  # 3개의 고유한 int_sentence가 채워지면 종료\n",
        "        break\n",
        "\n",
        "# 결과 출력\n",
        "print(\"사용자 질문:\", query)\n",
        "print(\"\\n상위 3개 결과:\")\n",
        "\n",
        "for result in top_results:\n",
        "    index = result.metadata['index']\n",
        "    print(f\"질문: {int_sentences[index]}\")\n",
        "    print(f\"답변: {int_responses[index]}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZyrwyUHkbSv",
        "outputId": "17cf0055-599a-4dc2-939f-ec5b090e312e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "사용자 질문: 우리나라 정부 형태가 뭐야?\n",
            "\n",
            "상위 3개 결과:\n",
            "질문: 우리나라의 정부 형태에 대해 알려주세요.\n",
            "답변: 우리나라의 정부 형태는 의원내각제적 요소를 가미한 대통령제 입니다.\n",
            "\n",
            "질문: 우리나라 대통령제에서의 의원내각제적 요소는 무엇인가요?\n",
            "답변: 1. 국무총리제, 2. 국회의원의 장관직 겸직 가능, 3. 행정부의 법률안 제출권입니다.\n",
            "\n",
            "질문: 총재 정부란 무엇인가요?\n",
            "답변: 총재 정부는 프랑스 혁명 당시 로베스피에르의 가혹한 공포 정치에 반발하여 일어난 정부로 5명의 총재가 행정을 담당하였습니다.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 사용자 쿼리-데이터셋 답변 유사도 검색"
      ],
      "metadata": {
        "id": "8KE_PDkZNx3y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "from langchain_core.documents import Document\n",
        "\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from langchain.retrievers import EnsembleRetriever\n",
        "from langchain_community.retrievers import BM25Retriever\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.schema import Document\n",
        "\n",
        "from kiwipiepy import Kiwi\n",
        "\n",
        "file_path = '중학사회(취합)_카테고리 추가.xlsx'\n",
        "df = pd.read_excel(file_path, engine='openpyxl')\n",
        "\n",
        "# 질문과 답변 리스트 생성\n",
        "questions = df['SENT_CONTENT'].tolist()\n",
        "int_sentences = df['INT_SENTENCE'].tolist()\n",
        "int_responses = df['INT_RESPONSE'].tolist()\n",
        "\n",
        "# Kiwi 형태소 분석기 설정\n",
        "kiwi = Kiwi()\n",
        "\n",
        "# 형태소 분석을 통해 문서를 토큰화하는 함수\n",
        "def kiwi_tokenize(text):\n",
        "    return [token.form for token in kiwi.tokenize(text)]\n",
        "\n",
        "# 문서와 쿼리를 토큰화\n",
        "tokenized_int_responses = [kiwi_tokenize(response) for response in int_responses]\n",
        "\n",
        "# 사용자 질문에 대한 카테고리 결정\n",
        "query = \"우리나라 정부 형태가 뭐야?\"\n",
        "category = '정치'\n",
        "\n",
        "# 특정 카테고리에 해당하는 문서만 필터링\n",
        "filtered_documents = []\n",
        "for i, response in enumerate(int_responses):\n",
        "    doc_category = df.loc[i, 'CATEGORY']\n",
        "    if doc_category == category:\n",
        "        filtered_documents.append(Document(page_content=response, metadata={'index': i, 'category': doc_category}))\n",
        "\n",
        "# BM25 모델 설정\n",
        "kiwi_bm25 = BM25Retriever.from_documents(filtered_documents, preprocess_func=kiwi_tokenize)\n",
        "\n",
        "# FAISS 설정\n",
        "hf_model_name = \"intfloat/multilingual-e5-large\"  # contextualized embedding model 사용\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=hf_model_name)\n",
        "faiss = FAISS.from_documents(filtered_documents, hf_embeddings).as_retriever()"
      ],
      "metadata": {
        "id": "L1uqB3oxL7Xl"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# EnsembleRetriever에서 검색 수행\n",
        "retriever = EnsembleRetriever(\n",
        "    retrievers=[kiwi_bm25, faiss],\n",
        "    weights=[0.3, 0.7],\n",
        "    search_type=\"mmr\",\n",
        ")\n",
        "\n",
        "# 검색 수행\n",
        "results = retriever.invoke(query)\n",
        "\n",
        "# 중복되지 않은 int_sentence를 저장할 set\n",
        "unique_sentences = set()\n",
        "\n",
        "# 상위 3개의 중복되지 않은 int_sentence를 찾는 리스트\n",
        "top_results = []\n",
        "\n",
        "# 결과에서 중복되지 않은 int_sentence만 저장\n",
        "for result in results:\n",
        "    index = result.metadata['index']  # 검색된 결과의 인덱스\n",
        "    if int_sentences[index] not in unique_sentences:\n",
        "        unique_sentences.add(int_sentences[index])  # 중복 제거\n",
        "        top_results.append(result)\n",
        "    if len(top_results) == 3:  # 3개의 고유한 int_sentence가 채워지면 종료\n",
        "        break\n",
        "\n",
        "# 결과 출력\n",
        "print(\"사용자 질문:\", query)\n",
        "print(\"\\n상위 3개 결과:\")\n",
        "\n",
        "for result in top_results:\n",
        "    index = result.metadata['index']\n",
        "    print(f\"질문: {int_sentences[index]}\")\n",
        "    print(f\"답변: {int_responses[index]}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NrpjJv8YL8Mb",
        "outputId": "ce1813de-cb12-4e60-d73e-1b4a6598a3ca"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "사용자 질문: 우리나라 정부 형태가 뭐야?\n",
            "\n",
            "상위 3개 결과:\n",
            "질문: 우리나라의 정부 형태에 대해 알려주세요.\n",
            "답변: 우리나라의 정부 형태는 의원내각제적 요소를 가미한 대통령제 입니다.\n",
            "\n",
            "질문: 대통령제와 의원 내각제의 차이점이 무엇인가요?\n",
            "답변: 대통령제는 입법부와 행정부가 엄격하게 분리된 정부 형태입니다.\n",
            "의원 내각제는 입법부와 행정부의 권한이 융합된 정부 형태입니다.\n",
            "대통령제와 의원 내각제를 구분하는 방법은 정부와 의회의 융합 정도를 보면 됩니다.\n",
            "정부와 의회가 철저하게 구분되어 있으면 대통령제, 정부와 의회가 융합되어 있으면 의원 내각제입니다.\n",
            "\n",
            "질문: 양원제와 단원제가 무엇인가요?\n",
            "답변: 양원제는 의회가 상원과 하원으로 나뉘어 각각 정무를 따로 맡는 국회 형태이고,\n",
            "단원제는 의회가 단일화되어 모든 정무를 일괄로 맡는 국회 형태를 일컫습니다.\n",
            "현재 우리나라는 상원과 하원으로 구분되지 않는 단원제입니다.\n",
            "\n"
          ]
        }
      ]
    }
  ]
}