Transformers,_what_can_they_do?.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dorbodwolf/huggingface_nlp_course/blob/main/Transformers%2C_what_can_they_do%3F.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgut00no7st6"
      },
      "source": [
        "# Transformers, what can they do?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_sMgEmPn7st8"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "UKZNQsZq7st-",
        "outputId": "de5a0a2e-194b-4a44-bbec-ef3280b68652",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.17.1 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.33.2 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fxz4-WUX7st_",
        "outputId": "a9b63f62-73cd-474d-e864-544d6d6e469e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199,
          "referenced_widgets": [
            "a6d5acb3d1cf4be8a632bbeffb6a38d3",
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            "c1db82ac542740a8ae037f5f126dec85",
            "420e9400dfad45059d356dda7586a01d",
            "820f221397914bfab4d224ee5653b37f",
            "c437dffa1adb4f7db65aefa5f19d61e9",
            "79cff59661074879b62526fb7d22d9a9",
            "c515940a60ce41c191ea2f55bb84935f",
            "2023555919cf4263ad370e01497dd2fc",
            "25640177b54f489a8fbcb4a75297fb13",
            "4361d576cd2547b3857c41d0b6f3c7a7",
            "127b437f279f4445b4bc2a7518555631",
            "f9f7d3d8b5bf4689b7e81a3ffa8a1592",
            "b4f849d0af1f4567a285aedf3fcc85b7",
            "7f4fadc5aa1b44efa9421c2c5a2b93f8",
            "c2f4e098f04a4d299d181838fad9a0f5",
            "cb4a771336a647988287c5cf6ac3c04e",
            "b3bf845a393a4142baa1256871e87e73",
            "c1b2c771c96e47c3b99cef393212d9ad",
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            "608115635ca544078eebe2af6cb667c0",
            "add221de73174036b1cea5d972d2bb04",
            "d5787c7b4df64e368819cb8ef8e5ab11",
            "75131f06ca134947b6956845033d2ee2",
            "09d6d49b71ea4cc786732bb6ffa76eac",
            "ed397fb6c0f5447189e3f025e9f90e9b",
            "e816bc7063e94dcea29b12f2267d7e5d",
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            "0248500ecbb7404f8acb44eb33998e79",
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            "2c7dc0f9081e46f98be8cea6b4e51b9d",
            "f3eff52f3b524d70bca03f40592a92d0",
            "c7f3f0bdf9b14aa7a9fd09d0eb70973e",
            "902c93d0ec5a4882843f34dbd885ea7b",
            "4c867f91a3154bdfa228e9003b73b459",
            "47fc49a5b3874029b8b7906383ec1df1",
            "a5329b8cfaab425982d6754b45b4e13a",
            "fcad0fcfbb8947c2940ca75a0b150759",
            "f648ab7729b445228544393085cafb63",
            "24066874194a4c129a1094a2ac173ccb",
            "fac9a9e79cd44d77b46033cc5bcc3368",
            "7cb6ed76bf6746f68dfd659f1b3da192",
            "ae5220dd26004eed9bbb9eb3d9238df8",
            "8d75aa23a9794b7cb4222e993a79f044"
          ]
        }
      },
      "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": "a6d5acb3d1cf4be8a632bbeffb6a38d3"
            }
          },
          "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": "127b437f279f4445b4bc2a7518555631"
            }
          },
          "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": "d5787c7b4df64e368819cb8ef8e5ab11"
            }
          },
          "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": "902c93d0ec5a4882843f34dbd885ea7b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9598048329353333}]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\")\n",
        "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J4Z9UoEC7st_",
        "outputId": "e2d16ad9-0cac-45a4-cefa-1290b22beb1d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9598048329353333},\n",
              " {'label': 'NEGATIVE', 'score': 0.9994558691978455}]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "classifier(\n",
        "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IXrV5bvD7suA",
        "outputId": "88540395-76a6-4c57-a57d-8a70f99d05b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": "execute_result",
          "data": {
            "text/plain": [
              "{'sequence': \"It's about 200km to Kyiv\",\n",
              " 'labels': ['direction', 'people', 'food'],\n",
              " 'scores': [0.8472875952720642, 0.12046723812818527, 0.03224518895149231]}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"zero-shot-classification\")\n",
        "classifier(\n",
        "    \"It's about 200km to Kyiv\",\n",
        "    candidate_labels=[\"food\", \"direction\", \"people\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9hmGYk4p7suA",
        "outputId": "aefce8e4-868d-4bfd-d0fd-526f07201017",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
            "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 manage your portfolio and your'},\n",
              " {'generated_text': 'In this course, we will teach you how to find \"a way to'}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "generator = pipeline(\"text-generation\")\n",
        "generator(\"In this course, we will teach you how to\", num_return_sequences=2, max_length=15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e0ZQIfiZ7suA",
        "outputId": "a540fd06-563d-4dc5-9582-03271595407e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': '本课程我们将教你 好 」 的 第 十 八 號 釋 憲 文 。 陳 慧 如 指 出, 本 來 就 是 「'},\n",
              " {'generated_text': '本课程我们将教你 們 大 多 數 的 朋 友 和 家 人 」 。 他 說 : 「 他 們 會 說 『 你'}]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "generator = pipeline(\"text-generation\", model=\"ckiplab/gpt2-base-chinese\", trust_remote_code=True)\n",
        "generator(\n",
        "    \"本课程我们将教你\",\n",
        "    max_length=30,\n",
        "    num_return_sequences=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FlICsu3m7suB",
        "outputId": "d6a1116b-eacf-4327-93f6-42c0bbb03620",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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': 0.19619806110858917,\n",
              "  'token': 30412,\n",
              "  'token_str': ' mathematical',\n",
              "  'sequence': 'This course will teach you all about mathematical models.'},\n",
              " {'score': 0.04052723944187164,\n",
              "  'token': 38163,\n",
              "  'token_str': ' computational',\n",
              "  'sequence': 'This course will teach you all about computational models.'},\n",
              " {'score': 0.03301795944571495,\n",
              "  'token': 27930,\n",
              "  'token_str': ' predictive',\n",
              "  'sequence': 'This course will teach you all about predictive models.'},\n",
              " {'score': 0.031941577792167664,\n",
              "  'token': 745,\n",
              "  'token_str': ' building',\n",
              "  'sequence': 'This course will teach you all about building models.'},\n",
              " {'score': 0.024522852152585983,\n",
              "  'token': 3034,\n",
              "  'token_str': ' computer',\n",
              "  'sequence': 'This course will teach you all about computer models.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "unmasker = pipeline(\"fill-mask\")\n",
        "unmasker(\"This course will teach you all about <mask> models.\", top_k=5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "unmasker = pipeline(\"fill-mask\", model='bert-base-cased')\n",
        "unmasker(\"This course will teach you all about [MASK] models.\", top_k=5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzp1o7dEgduz",
        "outputId": "6d3105db-e605-46ef-f860-4aa06140893a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.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": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.25963184237480164,\n",
              "  'token': 1648,\n",
              "  'token_str': 'role',\n",
              "  'sequence': 'This course will teach you all about role models.'},\n",
              " {'score': 0.09427252411842346,\n",
              "  'token': 1103,\n",
              "  'token_str': 'the',\n",
              "  'sequence': 'This course will teach you all about the models.'},\n",
              " {'score': 0.03386765345931053,\n",
              "  'token': 4633,\n",
              "  'token_str': 'fashion',\n",
              "  'sequence': 'This course will teach you all about fashion models.'},\n",
              " {'score': 0.025944063439965248,\n",
              "  'token': 1297,\n",
              "  'token_str': 'life',\n",
              "  'sequence': 'This course will teach you all about life models.'},\n",
              " {'score': 0.02306208200752735,\n",
              "  'token': 1292,\n",
              "  'token_str': 'these',\n",
              "  'sequence': 'This course will teach you all about these models.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Zk8PONHB7suB",
        "outputId": "c25c0f5e-e7a5-4b2a-a168-0f4e2abe236d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590,
          "referenced_widgets": [
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            "199a454abb2e4e1abdb8b136ea3a1364",
            "cedc7716fe9842e7b66f7c71aa2f013e"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/2.86k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82e9186bd5d74f22b1f5caab4237a103"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/407M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "692545457753409eb9bb1cb77b0206b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/301 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6c8237fd1754c3a9c67fd22b07a39dc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/110k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4735c748aaf44fcbbc662b7ce5671c10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41f10d7ff2f74c45b2bcaff07b239fb9"
            }
          },
          "metadata": {}
        },
        {
          "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': 'FW',\n",
              "  'score': 0.99986935,\n",
              "  'word': 'My name is Sylvain and I work at Hugging Face',\n",
              "  'start': 0,\n",
              "  'end': 45},\n",
              " {'entity_group': 'P',\n",
              "  'score': 0.99999595,\n",
              "  'word': 'in',\n",
              "  'start': 46,\n",
              "  'end': 48},\n",
              " {'entity_group': 'FW',\n",
              "  'score': 0.9967378,\n",
              "  'word': 'Brooklyn',\n",
              "  'start': 49,\n",
              "  'end': 57},\n",
              " {'entity_group': 'PERIODCATEGORY',\n",
              "  'score': 0.99999976,\n",
              "  'word': '.',\n",
              "  'start': 57,\n",
              "  'end': 58}]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "ner = pipeline(\"ner\", grouped_entities=True, model='ckiplab/bert-base-chinese-pos')\n",
        "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "4vWKsed-7suB",
        "outputId": "d8daef47-fa06-4b6e-e217-73b3560e7a3a",
        "colab": {
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          "height": 231,
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            "0ae5a88d072f4b4c916aaef7fcd799f3"
          ]
        }
      },
      "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": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/473 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "251db17b151f40cdb657e7844f6ff354"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/261M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc7d898b68f5480e889e96a81c4e6d69"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd38bfc00f674db39553c7e5e218d50b"
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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,
              "version_minor": 0,
              "model_id": "a16b579f6ff94ca0bcaf39e76f86a32c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6775c2b523f4822a90d3ff4d054e4b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.6949767470359802, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "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",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "aBTt2V7G7suB",
        "outputId": "226ebd10-fd11-4841-f95e-34d001f303c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266,
          "referenced_widgets": [
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      },
      "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"
          ]
        },
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            "text/plain": [
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              "Downloading pytorch_model.bin:   0%|          | 0.00/1.22G [00:00<?, ?B/s]"
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            ],
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          "metadata": {}
        },
        {
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            ],
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          "metadata": {}
        },
        {
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          "data": {
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            ],
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        {
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          "data": {
            "text/plain": [
              "[{'summary_text': ' The number of engineering graduates in the United States has declined in recent years . China and India graduate six and eight times as many traditional engineers as the U.S. does . Rapidly developing economies such as China continue to encourage and advance the teaching of engineering . There are declining offerings in engineering subjects dealing with infrastructure, infrastructure, the environment, and related issues .'}]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\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",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "wPAN8Ig87suB",
        "outputId": "48b8bf05-2535-406a-be27-01174d6d0ce1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': '近年来,美国发生了巨大变化。'}]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-zh\")\n",
        "translator(\" America has changed dramatically during recent years.\")"
      ]
    }
  ],
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