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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qU transformers accelerate einops langchain xformers bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "\n",
        "model_name = 'tiiuae/falcon-40b-instruct'\n",
        "\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "# set quantization configuration to load large model with less GPU memory\n",
        "# this requires the `bitsandbytes` library\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=bfloat16\n",
        ")\n",
        "\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    trust_remote_code=True,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map='auto'\n",
        ")\n",
        "model.eval()\n",
        "print(f\"Model loaded on {device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "LQeEpm-hI3jZ",
        "outputId": "894e168d-46c6-4179-c689-157364f338a2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/658 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f401507da0046a5aed134832c629c6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/configuration_RW.py:   0%|          | 0.00/2.51k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b-instruct:\n",
            "- configuration_RW.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b-instruct:\n",
            "- modelling_RW.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('//172.28.0.1'), PosixPath('http')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-ye7nn1z80zy7 --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7ffd354b76c4d7ea8af0994879f6d32"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02cfec694c084430b748f06a927d3578"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded on cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "1b61e69b5d4d4813a25ce718b99d65b6",
            "ca6c5ec9777847ac8cea217ea6b53858",
            "40453deb021343449bc3390d404cb414",
            "8c8adf7e17334e9693d7761d1330f560",
            "cdb446a658064f02b42cbd9a51da80a4",
            "f27a0ea1b240445299de0f54f4c9bb9a",
            "53ddbaa074784b1f877b1a10fa61dbdc",
            "a734d7e9668d4b5bb33844f623ac7120",
            "3cf5f2c2631e409bafddba07e28ff4e8",
            "b5ce3b8955014aee825436ddb83f3a4d",
            "8911031fb12a437e800981023b1b0e47",
            "9af12138eddd407ebefc285186151176",
            "1cf205d62e32454b95299764b9ab86f6",
            "7a096df8cc784e978b43fc69abb09337",
            "06ef666998f8453fb6d1590198127f8d",
            "647d68867b5247299d901cce1460baff",
            "38335691e3e84e4780a86560d2cefa3c",
            "694c8a50ec204e3eb3f0d344fcd90fc1",
            "90ce1bfd09c747eab16ed6165504f930",
            "bac77403a96a45e187fda2f3ecce1163",
            "93b2b9b5c1204f20a712d3ca8aa3c66a",
            "c76142643eb6463284df8574c5978e20",
            "fbd4904b976a498f863d01fd8a81efa0",
            "3c8b96be2c734b0b84486c99cd515e4a",
            "549ab7a5bcba46588bd84edc1c0ab821",
            "0cb81d4e30524019bd2e369ebdbf5f81",
            "219550ac9a9b4cb19648357786ff66cd",
            "d7ae97c2f51c49ac9ffeb0137a5889f7",
            "1a9decda9db94aebaaedec6b2d0684d1",
            "7641c0e9711e4e04b9b8e711694f8709",
            "671296b1bbad44e2b07d8dfebe386574",
            "957604c2576e4523b13a8336883b9249",
            "c8f23e5f1c9f4f068ee4d1441c0bed8f"
          ]
        },
        "id": "gI8gDVWBJLF4",
        "outputId": "8603b93b-3586-422e-cb1b-da350cf0736c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/220 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b61e69b5d4d4813a25ce718b99d65b6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9af12138eddd407ebefc285186151176"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/281 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbd4904b976a498f863d01fd8a81efa0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import StoppingCriteria, StoppingCriteriaList\n",
        "\n",
        "# we create a list of stopping criteria\n",
        "stop_token_ids = [\n",
        "    tokenizer.convert_tokens_to_ids(x) for x in [\n",
        "        ['Human', ':'], ['AI', ':']\n",
        "    ]\n",
        "]\n",
        "\n",
        "stop_token_ids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TXjzyWBJgIz",
        "outputId": "5bafa271-8fa6-4191-d994-4e820a9bcb8b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[23431, 37], [17362, 37]]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]\n",
        "stop_token_ids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oX4dV9zXJg-E",
        "outputId": "11be6167-e1e5-463a-e7e1-0695c4e3751d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([23431,    37], device='cuda:0'),\n",
              " tensor([17362,    37], device='cuda:0')]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import StoppingCriteria, StoppingCriteriaList\n",
        "\n",
        "# define custom stopping criteria object\n",
        "class StopOnTokens(StoppingCriteria):\n",
        "    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:\n",
        "        for stop_ids in stop_token_ids:\n",
        "            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():\n",
        "                return True\n",
        "        return False\n",
        "\n",
        "stopping_criteria = StoppingCriteriaList([StopOnTokens()])"
      ],
      "metadata": {
        "id": "z9qwZctJJjlu"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_text = transformers.pipeline(\n",
        "    model=model, tokenizer=tokenizer,\n",
        "    return_full_text=True,  # langchain expects the full text\n",
        "    task='text-generation',\n",
        "    # we pass model parameters here too\n",
        "    stopping_criteria=stopping_criteria,  # without this model rambles during chat\n",
        "    temperature=0.0,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max\n",
        "    max_new_tokens=512,  # mex number of tokens to generate in the output\n",
        "    repetition_penalty=1.1  # without this output begins repeating\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUEBTHeyJYJ8",
        "outputId": "c06f4c1e-148d-43e5-8a0b-3efd3128b677"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The model 'RWForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "llm = HuggingFacePipeline(pipeline=generate_text)"
      ],
      "metadata": {
        "id": "baO9qHWMJpfM"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain -qU langchain faiss-cpu sentence_transformers cohere"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HM7_KXLXJ1FD",
        "outputId": "bca67529-5671-440d-a503-b59ab5478fa4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = 'hf'\n",
        "os.environ['COHERE_API_KEY'] = 'kb'"
      ],
      "metadata": {
        "id": "uCS5LuwDOPnm"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import re\n",
        "import cohere\n",
        "from pathlib import Path\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.retrievers import ContextualCompressionRetriever\n",
        "from langchain.retrievers.document_compressors import CohereRerank\n",
        "from langchain.chains import RetrievalQAWithSourcesChain,ConversationalRetrievalChain"
      ],
      "metadata": {
        "id": "AQt89HTFJ7j-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gibOsbmdObeJ",
        "outputId": "97e24d67-94ac-49ef-d4be-5bf6af530231"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db_dir = '/content/drive/MyDrive/turkish_pro_OS/data/data/vector_stores/'\n",
        "model_name = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
        "sentence_embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
        "sentence_db_dir = db_dir + \"faiss_index_MCdocs_en_500_30_translated_nllb-200-distilled-600M_embedded_all-MiniLM-L6-v2\"\n",
        "print('SENTENCE_DB:', sentence_db_dir)\n",
        "db = FAISS.load_local(sentence_db_dir, sentence_embeddings)\n",
        "\n",
        "retriever  = db.as_retriever(search_type = 'similarity', search_kwargs= {\"k\":10})\n",
        "\n",
        "compressor = CohereRerank(model='rerank-multilingual-v2.0', top_n = 3)\n",
        "compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)\n",
        "\n",
        "qa = RetrievalQAWithSourcesChain.from_chain_type(\n",
        "    # llm=llm,\n",
        "    llm = llm,\n",
        "    retriever = compression_retriever,\n",
        "    return_source_documents = True,\n",
        "    chain_type = \"map_reduce\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aLEN9aFOKy0Z",
        "outputId": "39588586-1062-4fde-9d11-1d7aa5a685e1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SENTENCE_DB: /content/drive/MyDrive/turkish_pro_OS/data/data/vector_stores/faiss_index_MCdocs_en_500_30_translated_nllb-200-distilled-600M_embedded_all-MiniLM-L6-v2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question=\"If the employee leaves his job before the expiry of the contract or enters into the employment of another employer without complying with the notice period, the new employer is jointly responsible with the employee in what cases, alongside the employee, for the termination of the contract.\"\n",
        "\n",
        "result = qa({'question':question})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286,
          "referenced_widgets": [
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            "7eeee117f45246cab5bfc4a5c1d5c41c",
            "b2d88d390ba04d22b2ddcaa966c287f7",
            "f42a45adf756495fbe10375f3b236583",
            "773fb8c755fe4d26a0766db46bf7963e",
            "e9612b32413e4468829a65fd5142322e",
            "61306a018cd04f8596d810af9adcabcb",
            "45262645928d4436952b303e627bb886",
            "7ceb92812b5f47e39cb049c94a5c8631"
          ]
        },
        "id": "5uWCQJrIMw3X",
        "outputId": "2cbcaeef-a2a4-4fe1-a17b-cd30c23e92c5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1259: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f26bd40273934d8599689b1c603f3b36"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac11bf9c86bb4780b65a55d2097b9ec3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ba87d00ce5b48c9a26bfc627b5f64a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de6e945421fa4392bdbca8f76c5c6983"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (1901 > 1024). Running this sequence through the model will result in indexing errors\n",
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result['answer']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "Fouijg5qR2BS",
        "outputId": "6bb3cbf2-c653-4740-ce93-c75247d28296"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' The new employer is jointly responsible with the employee in certain cases for the termination of the contract.\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![system.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "ZcVDzGAFSe6s"
      }
    }
  ]
}