Youtube_Huggingface_Transformers.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PradipNichite/Youtube-Tutorials/blob/main/Youtube_Huggingface_Transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Agenda:**\n",
        "Using Huggingface Transformers pre-trained models\n",
        "\n",
        "**Resources:**\n",
        "https://huggingface.co/course/chapter2/2?fw=tf\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3TcK_MWNUcyh"
      }
    },
    {
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      "source": [
        "!pip install transformers -q"
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      "metadata": {
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      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pipeline"
      ],
      "metadata": {
        "id": "qjaRFbDTpNc2"
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      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\",device=0)\n",
        "classifier(\"Food was amazing\")"
      ],
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        {
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          "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"
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              "model_id": "6c59f3734d8f43b18208e9ac7cf78b5a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9998799562454224}]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Behind the pipeline.** <br>\n",
        "pipeline groups together three steps: preprocessing, passing the inputs through the model, and postprocessing:"
      ],
      "metadata": {
        "id": "icoRHDRXouU-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
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      ],
      "metadata": {
        "id": "PglogsGXnW4-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier(\"Moview was waste of time\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLnGyv0sfanb",
        "outputId": "b3cb62e1-20d1-4b91-f9aa-a44ae9d62d25"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'NEGATIVE', 'score': 0.9997876286506653}]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment\n",
        "classifier = pipeline(\"sentiment-analysis\",model=\"cardiffnlp/twitter-roberta-base-sentiment\",device=0)\n",
        "classifier(\"Food was amazing\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
          "referenced_widgets": [
            "2b7ccab8a8f547e59521c33f25f0bbd6",
            "1d9d6735c537407698c8f30ae5ab18b1",
            "9c913a7af274414ea98944d2103c21d1",
            "56ee3673a70d422f8f302665164bdf9b",
            "2367588a8bea4fef9b389aaeaf741f91",
            "86d7a6905713400084b73023cf227737",
            "7d8b60fffd7344a78d7ad987700a56fe",
            "eed1ce931cd64cacab970d6119bc88e7",
            "fb995770eaa6434a84cbb3e75c38500e",
            "3c8ea3eec3a34958823722932a46ed9e",
            "528e25484a6c4e37aaf0e8962b7df0c6",
            "41b7c3351529413a899c8b7606dd4516",
            "dbe62948c9d34abf858a6b77cc6254a9",
            "1d8acd6edc2741879ed9875ae8854c50",
            "c3fcbb133197491992a1f7c911fbbcc8",
            "43c8884f017f4de9b0dff26702ae5b39",
            "d4ecad2de78b4859b8917579259cf979",
            "86c47c8435b742f092dcb3a2bc95d512",
            "1b2065d45c024e0583f1d9b765ab17b1",
            "0252456d135842d0a6c30cc5c1cb31dd",
            "2a2d2dd867294112941bd6d4b80673af",
            "4006892afdfe48cfa42a8dd5f22e94af",
            "d9cc5d1a69c74a1f8cc0ed5d84a7b6e4",
            "73d0ae74f53f4b718437874c8e154fe7",
            "560a6dbda4cb4162946e7ff728e8e38d",
            "d289394dd1c24be4946c831416a42c17",
            "6a3bb20025ed42518900799b8cd2cfd0",
            "b59c7a77e5614d1ebf947a4488912999",
            "f83a905942394fe284927642fcb63d23",
            "beee9203c7494903b0d61e3ab32044e9",
            "a51eac6378544ded89163949c5850332",
            "56f7a9ba36b74c56b3802896e9a2b65d",
            "24560a99f93b4c4487a1cb1731a8acd2",
            "b48e197d400c4523bf30dbdcc7648a62",
            "c80247a2e6d54051a9132bb9e38828d5",
            "23efb9a1f8f44d49bdfedbf76e2703ec",
            "a10319f6a70d41119bf1125c1abdab13",
            "4624430f71244441a233434fa1ef1723",
            "311a2dfdcd61459bb231ecc523f14536",
            "10f569fbd89840b6b0d9a8e7257e73c1",
            "05e1f0ba18084b9eba65bb3ba067ac53",
            "d08ad8d1c75c401db98127e48b168cdf",
            "48468cae86ec45d8bb923daa9453d004",
            "2c55d9bbd2a04a9a94ea58f021ec16dd",
            "5f39d5d8d7004036a1697d4e67e8055d",
            "5f594a070e2c4f64ac6b5365897be8bc",
            "864a10d002a84632808f4591e19c2971",
            "8ec7f289871642c18eb7667620dd27e0",
            "db8a36ba5ce94f22b7dc82d2128a8bae",
            "cfea91ef32624d529f262d6156d4e44d",
            "7c0c84995c8f4d71a8fa4509f48ae092",
            "b812ee95463d4807885078b794f3d1f5",
            "18417b73d18d4fa9a132151eab8582d6",
            "db086deb8529401d869e89e40545f4cb",
            "a7afce7cc3604279b5a48831da45028f"
          ]
        },
        "id": "yYQRpF7gVZ1P",
        "outputId": "0586e2ab-2fd9-4c89-ea6b-ca6d19d7f82f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/747 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b7ccab8a8f547e59521c33f25f0bbd6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/499M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41b7c3351529413a899c8b7606dd4516"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9cc5d1a69c74a1f8cc0ed5d84a7b6e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b48e197d400c4523bf30dbdcc7648a62"
            }
          },
          "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": "5f39d5d8d7004036a1697d4e67e8055d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'LABEL_2', 'score': 0.9718621969223022}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier(\n",
        "    [\"Food was amazing\", \"Moview was waste of time\"]\n",
        ")"
      ],
      "metadata": {
        "id": "rkk6lxoNTqx5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1624b461-eef9-4c8d-9842-4ffd3c647a41"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'LABEL_2', 'score': 0.9718621969223022},\n",
              " {'label': 'LABEL_0', 'score': 0.9271152019500732}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question Answering**"
      ],
      "metadata": {
        "id": "RDlnqec9X3v1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "question_answerer = pipeline(\"question-answering\",device=0)\n",
        "# question_answerer(\n",
        "#     question=\"Who is Pradip?\",\n",
        "#     context=\"My name is Pradip and I work as Freelancer\",\n",
        "# )\n",
        "\n",
        "question_answerer(\n",
        "    question=\"Where does Pradip Work?\",\n",
        "    context=\"My name is Pradip and I work as Freelancer on Upwork\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfBTFABvU65q",
        "outputId": "4a8a3c69-8dfb-4f7b-bb21-055aa468c80f"
      },
      "execution_count": 8,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.9836946129798889, 'start': 46, 'end': 52, 'answer': 'Upwork'}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# ner = pipeline(\"ner\",device=0)\n",
        "ner = pipeline(\"ner\",aggregation_strategy=\"simple\",device=0)\n",
        "ner(\"Sundar Pichai is CEO of Google\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3H6O02pCUNir",
        "outputId": "3cba4ede-3d17-423a-89f4-042c1595405b"
      },
      "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": "execute_result",
          "data": {
            "text/plain": [
              "[{'entity_group': 'PER',\n",
              "  'score': 0.99562234,\n",
              "  'word': 'Sundar Pichai',\n",
              "  'start': 0,\n",
              "  'end': 13},\n",
              " {'entity_group': 'ORG',\n",
              "  'score': 0.9987669,\n",
              "  'word': 'Google',\n",
              "  'start': 24,\n",
              "  'end': 30}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "summarizer = pipeline(\"summarization\",device=0)\n",
        "text = \"\"\"\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",
        "summarizer(text, max_length=30, min_length=10)"
      ],
      "metadata": {
        "id": "8rFq_FCxX9cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246,
          "referenced_widgets": [
            "fe1bb2a220fc4eaf899eef453783d036",
            "1af3eff3ff074033bc61e7f066dbfe01",
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            "6fbd4dc40ea44f04a681506e81a11f16",
            "c9007e4a0c5c41898278e35008870435",
            "7b5fcdf8f19a4e268bdc7cb884c44f2e",
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            "42dc139531344e38b6d967e94e19ab2e",
            "8a3256e8fc6a485d82f67cc56cea5b33",
            "93481404f0dc4a26aa84459e7009d5f5",
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          ]
        },
        "outputId": "ba96c4d9-24dd-4d8c-94ea-bb3159256569"
      },
      "execution_count": 11,
      "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": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
            ],
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        },
        {
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          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.22G [00:00<?, ?B/s]"
            ],
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        {
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        {
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          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': ' America has changed dramatically during recent years . There are declining offerings in engineering subjects dealing with infrastructure,  the environment, and related issues .'}]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "generator = pipeline(\"text-generation\",device=0)\n",
        "generator(\"In this course, we will teach you how to\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318,
          "referenced_widgets": [
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        },
        "id": "_cDqeUDhUA7M",
        "outputId": "1b9c3702-9f26-4c10-bf74-3fdefd0ab721"
      },
      "execution_count": 12,
      "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:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
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        {
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          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
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        {
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          "data": {
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              "Downloading:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
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              "version_major": 2,
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              "model_id": "be258f6ab8c7411bb2c9eddf108f6d0a"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a38ce121417a4929ba16f88507ae65f6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1232: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 50 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` 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",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'In this course, we will teach you how to use OpenCV to create your own OpenCV API from the source code of the standard OpenCV projects such as openCV1.3.\\n\\nCourse Topics\\n\\nObjects to be used in'}]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w6IGz60SztRt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}