ch1_sec3.ipynb

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      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/mit10000/1667/blob/master/ch1_sec3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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        "!pip install \"transformers\""
      ],
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      },
      "execution_count": 1,
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          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m64.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m107.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m58.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.7.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentencepiece"
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        },
        "id": "h-9LWMgQHJln",
        "outputId": "dabef283-2267-4ad9-993d-9c832b6e4707"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#https://huggingface.co/learn/nlp-course/zh-CN/chapter1/3?fw=pt\n",
        "from transformers import pipeline"
      ],
      "metadata": {
        "id": "NA1Z1t7nm6ID"
      },
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      "outputs": []
    },
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      "source": [
        "from transformers import pipeline\n",
        "#简单的文本情感分类\n",
        "classifier = pipeline('sentiment-analysis')\n",
        "classifier(\"I am full of these funny claims.\")"
      ],
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        "id": "m8ZEOik0nEl9",
        "outputId": "51796cf5-dd37-4113-f515-61542d78684c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be95353daea5455a945a8f47e595a8e7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e1b3055b93147eebfa4fa8e18b91071"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8cde9829b51948a4b8b0f0fe0c94f67a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a32f55866c64010a82e4dac5d19ee99"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9995383024215698}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier(\"I have been waiting for the food for 3 hours.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bH2pCPJ1ntad",
        "outputId": "43a68b1d-b50e-43e5-c8be-e0d6a4cf5458"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'NEGATIVE', 'score': 0.9911050200462341}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#zero shot 零样本文本分类\n",
        "classifier = pipeline(\"zero-shot-classification\")\n",
        "classifier(\n",
        "    \"This is a course about the Transformers library\",\n",
        "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296,
          "referenced_widgets": [
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            "97eb85877e904d67b7257ac1e6ab069d",
            "305b31dc8a0947f282db8e41b76c8660",
            "4a592909b0734689892e2481d787e122",
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            "70beb8f80c56401db5f1d7ae621dd0ae",
            "6ea6969d8a8c4b448cd0b7f6237baa01",
            "2b6197be61d4490fb1233b6185c422b9",
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            "1f10662196d145e889f0040cbbcb4eee",
            "602c4842aed649fc8f9f848f45a743c0",
            "ef4dfd949a654a728118713afa966bdb",
            "0d53a8072f4440268106e382a0e8ade7",
            "f942bd04e7ee426c8fcf9d88e8b30c03",
            "f195e8c8d43942c6984899a0f00147b9",
            "66e7428632dc4f7d81f06712c50ec475",
            "4aff3e1c57b94063aeaedbc787e71ce7",
            "4efcf03469fa4079a5b7fe1588c28e71",
            "5d3606fe8b1549698519df91b95c6d8b",
            "c1e5a4158bb04a3194a36bbd6857f054",
            "a61f0d7e64c146d8b6ac9d1c18074ba0",
            "9908878a9ed4420a96de45fd706282f7",
            "017b6d16d28d4f1db74223a74752f39d",
            "010313235cf6401cb45c675202fb4267",
            "7a5d974a0a254cc39f98983257b57f7f",
            "ce01ec9efdf64fd6b717b936dc63eb32",
            "ac38421051bb455ebeb0812deb87236f",
            "934a7ba6c10e4ab789a5d3069c532044",
            "80c79abc8bba45109e2409e1df31580a",
            "2c80cf1c00ab43519292dcb6697ff574",
            "3ce23f96ce94480c8a93454ad2f110f0",
            "f3bc3eec08764c379db06a43ac27c8bd",
            "b22e770ee9534c60a9d07aa5c821e5eb",
            "12e605f0147b438e922e6efe40b1e0fe",
            "3ddb497a27f8489987dc95f5c3e7018b",
            "c08ab433db544f70b7cfb630fd57ca3b",
            "af061784a0284ba1b39d643b1b5ee7a7",
            "33f4526c2470481882e28f9deebf3e68"
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        },
        "id": "hgJW7AyUoPd8",
        "outputId": "5dedfc8e-ef97-49aa-cf1f-50aa0e120de6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to facebook/bart-large-mnli and revision c626438 (https://huggingface.co/facebook/bart-large-mnli).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a12496e8bd84f1f89ad6058400645fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90ab8392bafc4bafaab85cb630205322"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ea6969d8a8c4b448cd0b7f6237baa01"
            }
          },
          "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": "4aff3e1c57b94063aeaedbc787e71ce7"
            }
          },
          "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": "934a7ba6c10e4ab789a5d3069c532044"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sequence': 'This is a course about the Transformers library',\n",
              " 'labels': ['education', 'business', 'politics'],\n",
              " 'scores': [0.8445989489555359, 0.11197412759065628, 0.04342695698142052]}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#文本生成\n",
        "generator = pipeline(\"text-generation\")\n",
        "generator(\"The origin of the world war II is\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351,
          "referenced_widgets": [
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            "727516ad9a1148528d704b194d538e61",
            "f76f81ac37924d7eb5182e8b5594c853",
            "bf97f3e1edd2455fb3b6279803c921ca",
            "beaf202ea3ee49bba11e3cfa2b2c4bbd",
            "daa913440a8b433d94c3f604ac1e7d9d",
            "80711413ef2540f1bf0f34568b157676",
            "e5c555f2c82740dbb400550a405c420a",
            "858133990de34504a7dcf7706a15b122",
            "2f3aa4b86d53418195c2c77197939e3a",
            "7bf84f8902784842bc4b7a394422d8ea",
            "929d0a922f474fa3acb0de894b1c8aff",
            "9f829ab464bf48e18a18f9c6be71172e",
            "af6053694dd54ec9b9629bbafd48308d",
            "56a3ccb3de6940108fa9685978b9a4dc",
            "09c3c4c51fa443579fec8ff4297dc9c9",
            "422f23b279004d8091126be47d11b41b",
            "9288838ecee24722a60f947b73a6fdde",
            "98727babe0c2400a96f5d8bd2d3dae8f",
            "ef9a35b79ce243abb53e8c8099a9c854",
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          ]
        },
        "id": "XT0HzqgiotFA",
        "outputId": "a99d9c76-e881-4109-d14f-7d57aaa342cb"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to gpt2 and revision 6c0e608 (https://huggingface.co/gpt2).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "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": "eca9f259672743c991652634fb6a0278"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba37f84e3a2b4100818f31af9650efcf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a392f80994b04239ab0e88c1db94e063"
            }
          },
          "metadata": {}
        },
        {
          "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": "a80408726ed644d7a7a084b396ecfcc3"
            }
          },
          "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": "f7b6be88223948c6b59e107823b260ca"
            }
          },
          "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": "7bf84f8902784842bc4b7a394422d8ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (50) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'The origin of the world war II is disputed. And at the same time two other groups, the Allied forces and the Western armies were trying to fight their way out of the Axis controlled area. The first was the Allies, and this was the last'}]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#生成的中文完全不好，没有道理\n",
        "generator(\"今天天气很好，我们决定\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZV_H2z_2pB_Y",
        "outputId": "e3ebb159-5440-4b08-d6b7-44edd92402de"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': '今天天气很好，我们决定我们便我们也害是绌乃乙。不能又'}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
        "generator(\n",
        "    \"In this course, we will teach you how to\",\n",
        "    max_length=30,\n",
        "    num_return_sequences=2,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296,
          "referenced_widgets": [
            "39ca46fdda454e78a59878e62fc3fbf8",
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        "id": "nVwuXZubp5Hk",
        "outputId": "5e780f3d-f749-430d-9e1a-8f3c42665aad"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "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": "23003d55d6a54a96bfc033937de04365"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"In this course, we will teach you how to use a small DSLR to generate a single DSLR file from PHP. Since you don't want\"},\n",
              " {'generated_text': \"In this course, we will teach you how to perform this technique as a group. The technique works only in short bursts. We'll take advantage of\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#命名实体识别 (NER) 是一项任务，其中模型必须找到输入文本的哪些部分对应于诸如人员、位置或组织之类的实体。就是给词汇分类\n",
        "#在这里，模型正确地识别出 Sylvain 是一个人 (PER)，Hugging Face 是一个组织 (ORG)，而布鲁克林是一个位置 (LOC)。\n",
        "ner = pipeline(\"ner\", grouped_entities=True)\n",
        "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547,
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/998 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.33G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/60.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "c8882dab6562409faf5f2480f1053b28"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/token_classification.py:169: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"simple\"` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'entity_group': 'PER',\n",
              "  'score': 0.9981694,\n",
              "  'word': 'Sylvain',\n",
              "  'start': 11,\n",
              "  'end': 18},\n",
              " {'entity_group': 'ORG',\n",
              "  'score': 0.9796019,\n",
              "  'word': 'Hugging Face',\n",
              "  'start': 33,\n",
              "  'end': 45},\n",
              " {'entity_group': 'LOC',\n",
              "  'score': 0.9932106,\n",
              "  'word': 'Brooklyn',\n",
              "  'start': 49,\n",
              "  'end': 57}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WwAIZUCvsIbv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#填空，把mask地方填入词汇\n",
        "unmasker = pipeline(\"fill-mask\")\n",
        "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pflqmLkq2YW",
        "outputId": "76eed727-2c31-47d7-abca-d8de1695aaba"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilroberta-base and revision ec58a5b (https://huggingface.co/distilroberta-base).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaForMaskedLM: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.1961982101202011,\n",
              "  'token': 30412,\n",
              "  'token_str': ' mathematical',\n",
              "  'sequence': 'This course will teach you all about mathematical models.'},\n",
              " {'score': 0.040527306497097015,\n",
              "  'token': 38163,\n",
              "  'token_str': ' computational',\n",
              "  'sequence': 'This course will teach you all about computational models.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question_answerer = pipeline(\"question-answering\")\n",
        "question_answerer(\n",
        "    question=\"Where do I work?\",\n",
        "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
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        "id": "4a-GVQCWsJQt",
        "outputId": "43572f84-30ff-43b9-c5a6-1285c27ec6ce"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-cased-distilled-squad and revision 626af31 (https://huggingface.co/distilbert-base-cased-distilled-squad).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
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          },
          "metadata": {}
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        {
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.6949759721755981, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#文本摘要\n",
        "summarizer = pipeline(\"summarization\")\n",
        "summarizer(\n",
        "    \"\"\"\n",
        "    America has changed dramatically during recent years. Not only has the number of\n",
        "    graduates in traditional engineering disciplines such as mechanical, civil,\n",
        "    electrical, chemical, and aeronautical engineering declined, but in most of\n",
        "    the premier American universities engineering curricula now concentrate on\n",
        "    and encourage largely the study of engineering science. As a result, there\n",
        "    are declining offerings in engineering subjects dealing with infrastructure,\n",
        "    the environment, and related issues, and greater concentration on high\n",
        "    technology subjects, largely supporting increasingly complex scientific\n",
        "    developments. While the latter is important, it should not be at the expense\n",
        "    of more traditional engineering.\n",
        "\n",
        "    Rapidly developing economies such as China and India, as well as other\n",
        "    industrial countries in Europe and Asia, continue to encourage and advance\n",
        "    the teaching of engineering. Both China and India, respectively, graduate\n",
        "    six and eight times as many traditional engineers as does the United States.\n",
        "    Other industrial countries at minimum maintain their output, while America\n",
        "    suffers an increasingly serious decline in the number of engineering graduates\n",
        "    and a lack of well-educated engineers.\n",
        "\"\"\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246,
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            "20ffa4cc06964a49977817ef2f9f9b63",
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            "4542406bf0214b018d85165535a7bc18",
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            "f42cd1bf506d4091bb6d44089567a9ee",
            "10055c8b431744d88b02c76069d8ed95",
            "10821b5f87164db0b5564978bd255eb8"
          ]
        },
        "id": "HMO0veUjsS5m",
        "outputId": "2c57fc42-fd36-45a9-d1c9-e1cd33b02c97"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "4594f6b0f1ab4a64840d4fc7c814851c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.22G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "45cfbc284bb14c6681eedd2e177b26e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28159cbdcbc24903b795706670c1dc2f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "2bad40120dea4f2ea10aa0d0a4c77abb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "20ffa4cc06964a49977817ef2f9f9b63"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': ' America has changed dramatically during recent years . The number of engineering graduates in the U.S. has declined in traditional engineering disciplines such as mechanical, civil,    electrical, chemical, and aeronautical engineering . Rapidly developing economies such as China and India continue to encourage and advance the teaching of engineering .'}]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%debug\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e8-mKS1c-bOu",
        "outputId": "a7135a59-0a01-4a64-8cbb-58ace904f0df"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py\u001b[0m(725)\u001b[0;36mfrom_pretrained\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m    723 \u001b[0;31m                    \u001b[0;32mreturn\u001b[0m \u001b[0mtokenizer_class_py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\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[0;32m    724 \u001b[0;31m                \u001b[0;32melse\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--> 725 \u001b[0;31m                    raise ValueError(\n",
            "\u001b[0m\u001b[0;32m    726 \u001b[0;31m                        \u001b[0;34m\"This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m    727 \u001b[0;31m                        \u001b[0;34m\"in order to use this tokenizer.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\n",
            "ipdb> import torch\n",
            "ipdb> torch.__version__\n",
            "'2.0.1+cu118'\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "PYDEV DEBUGGER WARNING:\n",
            "sys.settrace() should not be used when the debugger is being used.\n",
            "This may cause the debugger to stop working correctly.\n",
            "If this is needed, please check: \n",
            "http://pydev.blogspot.com/2007/06/why-cant-pydev-debugger-work-with.html\n",
            "to see how to restore the debug tracing back correctly.\n",
            "Call Location:\n",
            "  File \"/usr/lib/python3.10/bdb.py\", line 361, in set_quit\n",
            "    sys.settrace(None)\n",
            "\n",
            "\n",
            "PYDEV DEBUGGER WARNING:\n",
            "sys.settrace() should not be used when the debugger is being used.\n",
            "This may cause the debugger to stop working correctly.\n",
            "If this is needed, please check: \n",
            "http://pydev.blogspot.com/2007/06/why-cant-pydev-debugger-work-with.html\n",
            "to see how to restore the debug tracing back correctly.\n",
            "Call Location:\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/debugger.py\", line 1075, in cmdloop\n",
            "    sys.settrace(None)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--KeyboardInterrupt--\n",
            "\n",
            "KeyboardInterrupt: Interrupted by user\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
        "translator(\"Ce cours est produit par Hugging Face.\")"
      ],
      "metadata": {
        "colab": {
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          "height": 258,
          "referenced_widgets": [
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        },
        "id": "A9zUXfFHsqxg",
        "outputId": "a61f3abd-fed2-41dd-8b76-cfd057423029"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "db64219a43374517ac8bd26fd8920379"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.34M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'This course is produced by Hugging Face.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
        "result = unmasker(\"This man works as a [MASK].\")\n",
        "print([r[\"token_str\"] for r in result])\n",
        "\n",
        "result = unmasker(\"This woman works as a [MASK].\")\n",
        "print([r[\"token_str\"] for r in result])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
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        },
        "id": "m0z-3SoqA16X",
        "outputId": "75374b3f-7503-4961-ba5e-9c703db61e04"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ccde323a1d84765b6ad8c5525758fc4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "191b91e0753d4c4bab3f634d24e7bed4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n",
            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a04f9ad66ba54f13b19598896b8182fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "848af1c59d9a45ea9b680490a1e77c61"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "6829673fda244ebcb5274f8f9dde432a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['carpenter', 'lawyer', 'farmer', 'businessman', 'doctor']\n",
            "['nurse', 'maid', 'teacher', 'waitress', 'prostitute']\n"
          ]
        }
      ]
    }
  ]
}