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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ludwigwittgenstein2/Research/blob/master/Hugging_Face_Tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqm6b7aUOf1x"
      },
      "source": [
        "#Hugging Face Tutorial \n",
        "\n",
        "1. Sentimenent Analysis\n",
        "2. Sequence Classification\n",
        "3. Extractive Question Answering\n",
        "4. Open Ended text generation\n",
        "4. Named Entity Recognition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtkH6D5iz0m_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd1e86ed-5ec6-45fd-a2fa-f7b878541ed2"
      },
      "source": [
        "!pip install transformers\n",
        "# https://huggingface.co/transformers/task_summary.html\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/98/87/ef312eef26f5cecd8b17ae9654cdd8d1fae1eb6dbd87257d6d73c128a4d0/transformers-4.3.2-py3-none-any.whl (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 11.5MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 51.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/23/2ddc317b2121117bf34dd00f5b0de194158f2a44ee2bf5e47c7166878a97/tokenizers-0.10.1-cp37-cp37m-manylinux2010_x86_64.whl (3.2MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 44.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp37-none-any.whl size=893262 sha256=09029f92808aabed05b15bcc0b5ce94ef0c6cf5370a68360d7fe7ea288692f33\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.10.1 transformers-4.3.2\n"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlS0q6sFfaHq"
      },
      "source": [
        "1.Sentiment Analysis \n",
        "\n",
        "We use this to find positive or negative sentiment in reviews, comments about customers \n",
        "\n",
        "2. Sequence Classification\n",
        "\n",
        "We use \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1JsFSm3ViVY"
      },
      "source": [
        "#Sentiment Analysis"
      ]
    },
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        "outputId": "361a8901-81e3-4caf-b6bd-45371b64f50e"
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      "source": [
        "#Import Modules\n",
        "from transformers import pipeline\n",
        "classifier = pipeline('sentiment-analysis')"
      ],
      "execution_count": 3,
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        {
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            "\n"
          ],
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        {
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    {
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      "source": [
        "\n",
        "classifier('We are very sad to show you the 😜 Transfomers Library')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "[{'label': 'NEGATIVE', 'score': 0.9984051585197449}]"
            ]
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          "metadata": {
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          "execution_count": 11
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      "source": [
        "results = classifier([\"We are very happy to show you the 🤫 Transfomers library\", \"we hope you don't hate it\"])\n",
        "\n",
        "for result in results: \n",
        "  print(f\"label:{result['label']}, with score:{round(result['score'], 4)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "label:POSITIVE, with score:0.9998\n",
            "label:POSITIVE, with score:0.832\n"
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xyj0-11QVnEZ"
      },
      "source": [
        "#Sequence Classification"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfONqUOs5IpH"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWwvWy2C5egE"
      },
      "source": [
        "classifier = pipeline('sentiment-analysis', model = \"nlptown/bert-base-multilingual-uncased-sentiment\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiKwuDGh6Leb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bd0fcb1b-5976-462a-a4f5-00f30cf0e3a4"
      },
      "source": [
        "classifier"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<transformers.pipelines.TextClassificationPipeline at 0x7fa1abdb57b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNwk7Y3j6X7e"
      },
      "source": [
        "model_name = \"nlptown/bert-base-multilingual-uncased-sentiment\"\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-iX863W9G__"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "model_name= \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzgMbijx-dBu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "c03de24a-285c-4a7e-a83d-7b980ff92086"
      },
      "source": [
        "#Tokenize them into numbers, next build a tensor out of them\n",
        "inputs = tokenizer(\"We are happy to show you the Transformers library.\")\n",
        "print(inputs)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'input_ids': [101, 2057, 2024, 3407, 2000, 2265, 2017, 1996, 19081, 3075, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEup4OYs_Ut0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "06142d19-c0c6-403f-8365-641f071476b5"
      },
      "source": [
        "pt_batch = tokenizer([\"We are happy to show you the Transfomers Library.\", \"We hope you don't hate it.\"],\n",
        "                     padding=True, truncation=True, return_tensors=\"pt\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hstUGfkbAChn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "75d204d7-1130-477a-fe05-b45ac0819ded"
      },
      "source": [
        "for key, value in pt_batch.items():\n",
        "  print(f\"{key}:{value.numpy().tolist()}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_ids:[[101, 2057, 2024, 3407, 2000, 2265, 2017, 1996, 9099, 14876, 16862, 3075, 1012, 102], [101, 2057, 3246, 2017, 2123, 1005, 1056, 5223, 2009, 1012, 102, 0, 0, 0]]\n",
            "attention_mask:[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9aTAs9YAM-v"
      },
      "source": [
        "#Now the input has been preprocessed by tokenizer\n",
        "pt_outputs = pt_model(**pt_batch)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Edi4VUaAqi1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4a45b2c7-78a0-4649-bc4a-85aefd7803be"
      },
      "source": [
        "print(pt_outputs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(tensor([[-4.1925,  4.4667],\n",
            "        [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>),)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5Om-s8-Asbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "62e052fc-96cf-4e69-dcf0-e9fdffc9001f"
      },
      "source": [
        "#Let's apply SoftMax to this\n",
        "import torch.nn.functional as F\n",
        "pt_predictions = F.softmax(pt_outputs[0], dim=-1)\n",
        "print(pt_predictions)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1.7351e-04, 9.9983e-01],\n",
            "        [5.3086e-01, 4.6914e-01]], grad_fn=<SoftmaxBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fsf9QcOXA6Wf"
      },
      "source": [
        "#tokenizer.save_pretrained(save_directory)\n",
        "#model.save_pretrained(save_directory)\n",
        "#We can load back this \n",
        "#tokenizer = AutoTokenizer.from_pretrained(save_directory)\n",
        "#model = TFAutoModel.from_pretrained(save_directory, from_pt=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8s8YD9ghWphB"
      },
      "source": [
        "#Sequence Classification\n",
        "\n",
        "We classify sequences according to given number of classes \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNyZB5cDBaTL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0079f813-3173-449b-c8e2-7c90d929ad68"
      },
      "source": [
        "#Let's try Sequence Classification\n",
        "from transformers import pipeline \n",
        "nlp = pipeline(\"sentiment-analysis\")\n",
        "result = nlp(\"I hate you\")[0]\n",
        "print(f\"label:{result['label']},with score:{round(result['score'],4)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "label:NEGATIVE,with score:0.9991\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kn-I4KaXC21N"
      },
      "source": [
        "#Sequence Classificaiton \n",
        "#1.Instantiate Tokenizer and Model from Checkpoint. Model is BERT\n",
        "#2. We Build a Sequence \n",
        "#3. Pass Sequence through the Model \n",
        "#4. Compute Softmax of Result\n",
        "#5. Print Results "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HoyT7OIIHucO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283,
          "referenced_widgets": [
            "e5206b0a5d3d4d53ab7dc24e1255952e",
            "2b318d992b3946da976068170f3a992b",
            "e180ca76dd4248ffa466a366736baf51",
            "b81acf06415b4041842c4ba7b4fbcc29",
            "b97cf531217c44b5afee24e909b1d96b",
            "15d250fa659f4526952afcfcf1ed4f05",
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            "96c64dca910949d297d0907bfcd336f0",
            "d160c32ba1934b709ab31626dc347079",
            "b5a778db27c24922ac9bed3ad7a79289",
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            "00c658a7d8b14c6081e0e1ead5e5d4de",
            "f7af088991ac4a5c8a21779f6016bedc",
            "d63f47631e1e4c57855919f8cb406558",
            "a1549b4d36a14e19a126bf25d904a4f1",
            "2d39fb6731774f3190d4b97ee24c7103",
            "ce2351db97e64661a50f4a497faaacef",
            "4af991454a374e228ca66b4a3792dd08",
            "5bb31043541c46d582ae37871e1f2ec2",
            "dd978fade39e474191b2aecf22b663e7",
            "f4d4af54571640b886e3aa17f53119f1",
            "4870a5ac970c4394801ea87d9aa63e6a"
          ]
        },
        "outputId": "854a6888-5df3-4952-c005-3db50c6f855b"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased-finetuned-mrpc\")\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased-finetuned-mrpc\")\n",
        "\n",
        "classes = [\"not paraphrase\", \"is paraphrase\"]\n",
        "\n",
        "sequence_0 = \"The company HuggingFace is based in New York City\"\n",
        "sequence_1 = \"Apples are especially bad for your health\"\n",
        "sequence_2 = \"HuggingFace's headquarters are situated in Manhattan\"\n",
        "\n",
        "paraphrase = tokenizer.encode_plus(sequence_0, sequence_2, return_tensors=\"pt\")\n",
        "not_paraphrase = tokenizer.encode_plus(sequence_0, sequence_1, return_tensors=\"pt\")\n",
        "\n",
        "paraphrase_classification_logits = model(**paraphrase)[0]\n",
        "not_paraphrase_classification_logits = model(**not_paraphrase)[0]\n",
        "\n",
        "paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]\n",
        "not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]\n",
        "\n",
        "print(\"Should be paraphrase\")\n",
        "for i in range(len(classes)):\n",
        "    print(f\"{classes[i]}: {round(paraphrase_results[i] * 100)}%\")\n",
        "\n",
        "print(\"\\nShould not be paraphrase\")\n",
        "for i in range(len(classes)):\n",
        "    print(f\"{classes[i]}: {round(not_paraphrase_results[i] * 100)}%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5206b0a5d3d4d53ab7dc24e1255952e",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d160c32ba1934b709ab31626dc347079",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Should be paraphrase\n",
            "not paraphrase: 10%\n",
            "is paraphrase: 90%\n",
            "\n",
            "Should not be paraphrase\n",
            "not paraphrase: 94%\n",
            "is paraphrase: 6%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rZN-Ii1XQHZ"
      },
      "source": [
        "#Extractive Question Answering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-chrITANleH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
            "f6e877542e59454686f6b283615ff347",
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            "493b64f57abc4e8bbf47ac71561abe9f",
            "7a05784c8d7f4cf6814874f10ef989cd",
            "1d3f3e2d47154a3e993169fb55617237",
            "fae8dc7d655247558e730a792db195af",
            "141ede2794f045c29aed8e990dbdb88c",
            "8aa8af977a9b4e049419bb74c625762b",
            "66c7e66d39fd4f7f8306a6b31c6e2315",
            "9db58c07412c42238effaba36b9d8e13",
            "5ff5c83183fb4dbfbd2031d5b71de724"
          ]
        },
        "outputId": "f15e25ed-e77f-46cb-985e-cf655cd2904a"
      },
      "source": [
        "#Extractive Question Answering\n",
        "from transformers import pipeline \n",
        "\n",
        "nlp = pipeline(\"question-answering\")\n",
        "\n",
        "context = r\"\"\"\n",
        "Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a question answering dataset is the SQuAD dataset, \n",
        "which is entirely based on that task.\n",
        "\"\"\"\n",
        "result = nlp(question=\"What is extractive question answering?\", context=context)\n",
        "print(f\"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6e877542e59454686f6b283615ff347",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=473.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:1324: FutureWarning: The `max_len` attribute has been deprecated and will be removed in a future version, use `model_max_length` instead.\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Answer: 'the task of extracting an answer from a text given a question.', score: 0.59, start: 34, end: 96\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2puIYwCCNU3B"
      },
      "source": [
        "#Another Example # Extractive Question Answering\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "\n",
        "text = r\"\"\"\n",
        "🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose\n",
        "... architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural\n",
        "... Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between\n",
        "... TensorFlow 2.0 and PyTorch.\n",
        "\"\"\"\n",
        "\n",
        "questions = [\"How many pretrained models are available in Transformers?\", \n",
        "             \"What does Transformers provide?\", \n",
        "             \"Transformers provide interoperability between which frameworks?\"]\n",
        "             "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKL2B43lQvcs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "d864e493-147c-44c5-89f2-90766c93e5d1"
      },
      "source": [
        "for question in questions:\n",
        "    inputs = tokenizer(question, text, add_special_tokens=True, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"].tolist()[0]\n",
        "    text_tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
        "    answer_start_scores, answer_end_scores = model(**inputs)\n",
        "    answer_start = torch.argmax(\n",
        "        answer_start_scores\n",
        "    )  # Get the most likely beginning of answer with the argmax of the score\n",
        "    answer_end = torch.argmax(answer_end_scores) + 1  # Get the most likely end of answer with the argmax of the score\n",
        "    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))\n",
        "    print(f\"Question: {question}\")\n",
        "    print(f\"Answer: {answer}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Question: How many pretrained models are available in Transformers?\n",
            "Answer: over 32 +\n",
            "Question: What does Transformers provide?\n",
            "Answer: general - purpose . . . architectures\n",
            "Question: Transformers provide interoperability between which frameworks?\n",
            "Answer: tensorflow 2 . 0 and pytorch\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NJjBdjIRWus"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEa5HBAATKl8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316,
          "referenced_widgets": [
            "447f027213da4c24a83d36d6ebb75005",
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            "5072638820ef4b74a52e62d37a2298a7",
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            "4d29885094174ca58bbe974c94520ade",
            "9bcb348144d646f7b079beb5142e13ae",
            "76ee32da87264d2aaa6e68c6e1b40870",
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            "e422e5d88dbc4061989f3856bc749547",
            "34c22c872a6740ccbd6a2c66692e19f6",
            "de42afc54c5f4c718d3477442124aa74",
            "f7f04870b15841bca9d193edfd6b55e7"
          ]
        },
        "outputId": "8cae50a7-e7ea-4a42-e6ba-b01687310c38"
      },
      "source": [
        "#Masked Language Modeling\n",
        "\n",
        "from transformers import pipeline \n",
        "nlp = pipeline(\"fill-mask\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "447f027213da4c24a83d36d6ebb75005",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=480.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bd40dff4ea46434e834eb9ca4379f9df",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898823.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "22117df084804a82a52fbb1f2cbca15e",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43400e24b6b849c689f1209ad130c9f6",
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d29885094174ca58bbe974c94520ade",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=331070498.0, style=ProgressStyle(descri…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of RobertaForMaskedLM were not initialized from the model checkpoint at distilroberta-base and are newly initialized: ['lm_head.decoder.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXdUJxqfTdTE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "58ce20b5-5c70-463e-8d3a-a7b929ba83fd"
      },
      "source": [
        "from pprint import pprint \n",
        "pprint(nlp(f\"HuggingFace is creating a {nlp.tokenizer.mask_token} that the community uses to solve NLP tasks.\"))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[{'score': 0.17927460372447968,\n",
            "  'sequence': '<s>HuggingFace is creating a tool that the community uses to '\n",
            "              'solve NLP tasks.</s>',\n",
            "  'token': 3944,\n",
            "  'token_str': 'Ġtool'},\n",
            " {'score': 0.1134939044713974,\n",
            "  'sequence': '<s>HuggingFace is creating a framework that the community uses '\n",
            "              'to solve NLP tasks.</s>',\n",
            "  'token': 7208,\n",
            "  'token_str': 'Ġframework'},\n",
            " {'score': 0.05243545398116112,\n",
            "  'sequence': '<s>HuggingFace is creating a library that the community uses to '\n",
            "              'solve NLP tasks.</s>',\n",
            "  'token': 5560,\n",
            "  'token_str': 'Ġlibrary'},\n",
            " {'score': 0.03493543714284897,\n",
            "  'sequence': '<s>HuggingFace is creating a database that the community uses '\n",
            "              'to solve NLP tasks.</s>',\n",
            "  'token': 8503,\n",
            "  'token_str': 'Ġdatabase'},\n",
            " {'score': 0.02860247902572155,\n",
            "  'sequence': '<s>HuggingFace is creating a prototype that the community uses '\n",
            "              'to solve NLP tasks.</s>',\n",
            "  'token': 17715,\n",
            "  'token_str': 'Ġprototype'}]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3fNnZtSTqnW"
      },
      "source": [
        "#Attribute Error! \n",
        "\n",
        "from transformers import AutoModelWithLMHead, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-cased\")\n",
        "model = AutoModelWithLMHead.from_pretrained(\"distilbert-base-cased\")\n",
        "\n",
        "sequence = f\"Distilled models are smaller than the models they mimic. Using them instead of the large versions would help {tokenizer.mask_token} our carbon footprint.\"\n",
        "input = tokenizer.encode(sequence, return_tensors=\"pt\")\n",
        "mask_token_index = torch.where(input == tokenizer.mask_token_id)[1]\n",
        "token_logits = model(input).logits\n",
        "\n",
        "mask_token_logits = token_logits[0, mask_token_index, :]\n",
        "top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QN-sQ5mDYIs9"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ksv6g-WVCuL"
      },
      "source": [
        "#Causal Language Modelling \n",
        "#Attribute Error, Torch Update\n",
        "\n",
        "from transformers import AutoModelWithLMHead, AutoTokenizer, top_k_top_p_filtering\n",
        "import torch \n",
        "from torch.nn import functional as F \n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
        "model = AutoModelWithLMHead.from_pretrained(\"gpt2\")\n",
        "\n",
        "sequence = f\"Trump or Biden will win the Election, Who will win\"\n",
        "input_ids = tokenizer.encode(sequence, return_tensors=\"pt\")\n",
        "\n",
        "next_token_logits = model(input_ids).logits[:,-1,:]\n",
        "filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)\n",
        "\n",
        "probs = F.softmax(filtered_next_token_logits, dim=-1)\n",
        "next_token = torch.multinomial(probs, num_samples=1)\n",
        "\n",
        "generated = torch.cat([])\n",
        "filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)\n",
        "\n",
        "#sample\n",
        "probs = F.softmax(siltered_next_token_logits, dim=-1)\n",
        "next_token = torch.multinomial(probs, num_samples=1)\n",
        "generated = torch.cat([input_ids, next_token], dim=-1)\n",
        "\n",
        "resulting_string = tokenizer.decode(generated.tolist()[0])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVt68JiFRxZO"
      },
      "source": [
        "# Text Generation\n",
        "Open Ended Text Generation\n",
        "  Goal is to create coherent portion of text in given    context\n",
        "\n",
        "Text Generation is possible with GPT-2, OpenAi-GPT, CTRL, PyTorch, Tensorflow\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rch9AIYZYbqr"
      },
      "source": [
        "from transformers import pipeline\n",
        "text_generator = pipeline(\"text-generation\")\n",
        "print(text_generator(\"As far as I am concerned, I will\", max_length=50, do_sample=False))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39sD-NQ8YmQl"
      },
      "source": [
        "from transformers import AutoModelWithLMHead, AutoTokenizer\n",
        "\n",
        "model = AutoModelWithLMHead.from_pretrained(\"xlnet-base-cased\")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"xlnet-base-cased\")\n",
        "\n",
        "\n",
        "# Padding text helps XLNet with short prompts \n",
        "\n",
        "PADDING_TEXT = \"\"\"In 1991, the remains of Russian Tsar Nicholas II and his family\n",
        "(except for Alexei and Maria) are discovered.\n",
        "The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the\n",
        "remainder of the story. 1883 Western Siberia,\n",
        "a young Grigori Rasputin is asked by his father and a group of men to perform magic.\n",
        "Rasputin has a vision and denounces one of the men as a horse thief. Although his\n",
        "father initially slaps him for making such an accusation, Rasputin watches as the\n",
        "man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n",
        "the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\n",
        "with people, even a bishop, begging for his blessing. <eod> </s> <eos>\"\"\"\n",
        "\n",
        "\n",
        "prompt = \"Today the weather is really nice and I am planning on \"\n",
        "\n",
        "inputs = tokenizer.encode(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors=\"pt\")\n",
        "\n",
        "prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))\n",
        "\n",
        "\n",
        "outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)\n",
        "\n",
        "generated = prompt + tokenizer.decode(outputs[0])[prompt_length:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0tR1zjJY8K-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "05fc4d68-489e-41d2-d4b2-8e79c36fbf69"
      },
      "source": [
        "print(generated)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Today the weather is really nice and I am planning on anning on posting a daily blog entry in the middle of the day as it will take less than a couple of hours to make it back to the hotel to clean up the room for the day. I have a few extra pictures this week too, but I don't have it to go to the bathroom that is the most important thing. I am hoping the next day has a\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hZUKCrERavN"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QM7B6WylZYkG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzA8zdgtaDsv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-9iLcLSiI9m"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qK4vnLXQU1uI"
      },
      "source": [
        "# Named Entity Recognition\n",
        "\n",
        "O, Outside of a named entity\n",
        "\n",
        "B-MIS, Beginning of a miscellaneous entity right after another miscellaneous entity\n",
        "\n",
        "I-MIS, Miscellaneous entity\n",
        "\n",
        "B-PER, Beginning of a person’s name right after another person’s name\n",
        "\n",
        "I-PER, Person’s name\n",
        "\n",
        "B-ORG, Beginning of an organisation right after another organisation\n",
        "\n",
        "I-ORG, Organisation\n",
        "\n",
        "B-LOC, Beginning of a location right after another location\n",
        "\n",
        "I-LOC, Location"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRBbW3lJomXA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262,
          "referenced_widgets": [
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            "8ca12a37ef204a13843a0dbc3bf7bdd8",
            "199e6671e20745a1b25dc0a8d143e735",
            "31c36e48101e4e6381a051b9653b06a9",
            "a624cfe7312b492d8174e70c11cfd374",
            "17a87e903cc348e8902b5cf7a954b2d5",
            "26d3817556ac48ba89b5eeb76d626147",
            "1eb0d530d19f4004bbfeca19d6516d02",
            "a14ef658aeeb400bbdd25dd5d430b455",
            "e9a4436daca441399900178d8353c3b1",
            "1171f991f21d46b2a3c974206eb7cc48",
            "568501b7c52e423883572b618d34786e"
          ]
        },
        "outputId": "91230079-84e2-40ef-cf78-8b2e0a27dfb3"
      },
      "source": [
        "from transformers import pipeline\n",
        "nlp = pipeline(\"ner\")\n",
        "\n",
        "sequence = \"Who will win the Election? Biden or Trump? We are excited to see\"\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3b295a0c48c40afa7871f70e8aaed91",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=998.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "204737a9d5464ac9ab9c608c29d729c9",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1334448817.0, style=ProgressStyle(descr…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "ee798266a9b547429dec099b38f26006",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5dae6785c2540d8ada65cba6d1cd0ce",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=60.0, style=ProgressStyle(description_w…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSAKZ7Udo3o2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23faf3f3-360a-4670-fdb2-2b9871b1da22"
      },
      "source": [
        "print(nlp(sequence))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[{'word': 'Election', 'score': 0.9869940280914307, 'entity': 'I-MISC', 'index': 5}, {'word': 'B', 'score': 0.9516230225563049, 'entity': 'I-PER', 'index': 7}, {'word': '##iden', 'score': 0.9853422045707703, 'entity': 'I-PER', 'index': 8}, {'word': 'Trump', 'score': 0.9972804188728333, 'entity': 'I-PER', 'index': 10}]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q69a0ZIMpjqp"
      },
      "source": [
        "from transformers import pipeline \n",
        "\n",
        "nlp = pipeline(\"ner\")\n",
        "\n",
        "sequence = \"Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very\" \"close to the Manhattan Bridge which is visible from the window.\"\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTlCiUPyGoB2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "fe32ceef-c31c-4492-a5cc-97d2912e3898"
      },
      "source": [
        "print(nlp(sequence))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[{'word': 'Hu', 'score': 0.9995632767677307, 'entity': 'I-ORG', 'index': 1}, {'word': '##gging', 'score': 0.9915938377380371, 'entity': 'I-ORG', 'index': 2}, {'word': 'Face', 'score': 0.9982671737670898, 'entity': 'I-ORG', 'index': 3}, {'word': 'Inc', 'score': 0.9994403719902039, 'entity': 'I-ORG', 'index': 4}, {'word': 'New', 'score': 0.9994346499443054, 'entity': 'I-LOC', 'index': 11}, {'word': 'York', 'score': 0.9993270635604858, 'entity': 'I-LOC', 'index': 12}, {'word': 'City', 'score': 0.9993864893913269, 'entity': 'I-LOC', 'index': 13}, {'word': 'D', 'score': 0.9825621843338013, 'entity': 'I-LOC', 'index': 19}, {'word': '##UM', 'score': 0.936983048915863, 'entity': 'I-LOC', 'index': 20}, {'word': '##BO', 'score': 0.8987101316452026, 'entity': 'I-LOC', 'index': 21}, {'word': 'Manhattan', 'score': 0.9758241176605225, 'entity': 'I-LOC', 'index': 29}, {'word': 'Bridge', 'score': 0.9902493953704834, 'entity': 'I-LOC', 'index': 30}]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L26aPuZMG8jX"
      },
      "source": [
        "from transformers import AutoModelForTokenClassification, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "label_list = [\n",
        "\"O\",       # Outside of a named entity\n",
        "\"B-MISC\",  # Beginning of a miscellaneous entity right after another miscellaneous entity\n",
        "\"I-MISC\",  # Miscellaneous entity\n",
        "\"B-PER\",   # Beginning of a person's name right after another person's name\n",
        "\"I-PER\",   # Person's name\n",
        "\"B-ORG\",   # Beginning of an organisation right after another organisation\n",
        "\"I-ORG\",   # Organisation\n",
        "\"B-LOC\",   # Beginning of a location right after another location\n",
        "\"I-LOC\"    # Location\n",
        "]\n",
        "sequence = \"Hugging Face is a company based in New York City. It's HQ is in Manhattan Bridge\"\n",
        "tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))\n",
        "inputs = tokenizer.encode(sequence, return_tensors=\"pt\")\n",
        "outputs = model(inputs).logits\n",
        "predictions = torch.argmax(outputs, dim=2)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsuymwZlI9H8"
      },
      "source": [
        "\n",
        "#print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].numpy())])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMguEj5aQVVB"
      },
      "source": [
        "#Summarizer\n",
        "\n",
        "Summarizing the text into shorter text \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_bfQ2qgsJGHP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262,
          "referenced_widgets": [
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            "f9252214d8644117915fbf86b99a4d4b",
            "504bba656d62407dab7f6cc4d56b97fa",
            "573a15da778f4bbab91abb0117d1e436",
            "68e26c65b1f5446890632e811603991b",
            "7bfbe354deb44ea49e6ef9141779a2ca",
            "96d4e60924eb4d1b8fb172192dfc8453",
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            "8aa9a237093f4adf851b389c99742d7d",
            "dae5bc03fcf2448881444e9c6e6e7998",
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            "1d8e39e959aa4b8699bcbff03a39f41c",
            "bdb0099fe8a0414b98129bcc1c55f03b",
            "9b3f66ae1c6f45d989b5fb78e37860f6",
            "eb3892d568a641488cde0721fe7d3db9",
            "ee4c90cd0b1847edbcf815c42247fdb3",
            "1cb1eada87a247d5b7a0f4561a31f103",
            "ed4aa88a9433402c80f98e61e5077fc2",
            "bcea7d3e088d48218f895c4dcb3d439c",
            "219af60c63234dd88a50ec43035df02f",
            "2a283c9f81374d8194ae064891c7d7aa",
            "721713dea5b149dcb7aa8903274d34c6",
            "05aeb5a99f2d42eca99938f28ddd2483",
            "a9337e1f69784ca9bf3e701ba0837c38"
          ]
        },
        "outputId": "c0f1cc41-66d6-4032-dad8-5b18e790d5b5"
      },
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "summarizer = pipeline(\"summarization\")\n",
        "\n",
        "ARTICLE = \"\"\"\n",
        "New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.\n",
        " A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.\n",
        " Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared \"I do\" five more times, sometimes only within two weeks of each other.\n",
        " In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her \"first and only\" marriage.\n",
        " Barrientos, now 39, is facing two criminal counts of \"offering a false instrument for filing in the first degree,\" referring to her false statements on the\n",
        " 2010 marriage license application, according to court documents.\n",
        " Prosecutors said the marriages were part of an immigration scam.\n",
        " On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.\n",
        " After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective\n",
        " Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.\n",
        " All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.\n",
        " Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.\n",
        " Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.\n",
        " The case was referred to the Bronx District Attorney\\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\\'s\n",
        " Investigation Division. Seven of the men are from so-called \"red-flagged\" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.\n",
        " Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.\n",
        " If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.\n",
        " \"\"\"\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1621.0, style=ProgressStyle(description…"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7bfbe354deb44ea49e6ef9141779a2ca",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8aa9a237093f4adf851b389c99742d7d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=26.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1cb1eada87a247d5b7a0f4561a31f103",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1222317369.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sZAgQp7JbMB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "f3a345a8-6edf-4481-fa64-30dd16340cc5"
      },
      "source": [
        "print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[{'summary_text': ' Liana Barrientos has been married 10 times, nine of them between 1999 and 2002 . Prosecutors say the marriages were part of an immigration scam . She is believed to still be married to four men and at one time, she was married to eight men at once .'}]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8igDMzCGUPK1"
      },
      "source": [
        "#Translation\n",
        "\n",
        "From English to German"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5wdlzEQK6_N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b3fe62c-7c33-4038-c26d-99d75e361c93"
      },
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "translator = pipeline(\"translation_en_to_de\")\n",
        "print(translator(\"Who will win the Election? Biden or Trump?\", max_length=40))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of T5Model were not initialized from the model checkpoint at t5-base and are newly initialized: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[{'translation_text': 'Wer wird die Wahl gewinnen: Biden oder Trump?'}]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}