BERT_Question_Answering.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ludwigwittgenstein2/Research/blob/master/BERT_Question_Answering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "#Question Answering with Fine-Tuned BERT \n",
        "                        by Rick Rejeleene"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZvIxOmUQ0fK"
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        ""
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    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "1) BERT Input Format \n",
        "\n",
        "2) Start & End Token Classifiers\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkXvi0e-Q8As"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
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      "metadata": {
        "id": "qC4gl5v3Q8Pg",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ad52b49-3a63-41a2-997c-7da3e5bd1e49"
      },
      "source": [
        "#Install HuggingFace Transformer Library \n",
        "\n",
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7c/a5/78be1a55b2ac8d6a956f0a211d372726e2b1dd2666bb537fea9b03abd62c/tokenizers-0.9.2-cp36-cp36m-manylinux1_x86_64.whl (2.9MB)\n",
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            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Collecting sentencepiece!=0.1.92\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/2d/6d4ca4bef9a67070fa1cac508606328329152b1df10bdf31fb6e4e727894/sentencepiece-0.1.94-cp36-cp36m-manylinux2014_x86_64.whl (1.1MB)\n",
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            "Collecting sacremoses\n",
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            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.17.0)\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-cp36-none-any.whl size=893257 sha256=1dd271321aa732f4c79a98e43891a4d76cbd0c9cce81708daa6a6854a8b748a2\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sentencepiece, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.94 tokenizers-0.9.2 transformers-3.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Oj6v17HRD2Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115,
          "referenced_widgets": [
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            "8bc32fc35698433dbc3c1522220d03dd",
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          ]
        },
        "outputId": "46b1df10-f714-479c-a525-b3dc9b1983b0"
      },
      "source": [
        "#Load Fine-Tuned BERT-Large\n",
        "\n",
        "#https://huggingface.co/transformers/pretrained_models.html\n",
        "#Contains Pre-trained models, large uncased means, pre trained on lower case english text\n",
        "#BERT Large is fine tuned for SQuaD benchmark\n",
        "#BERT-Large has 24 layers, embedding size 1024 and 340 M parameters\n",
        "\n",
        "\n",
        "from transformers import BertForQuestionAnswering\n",
        "\n",
        "model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f330ce3633bb453b9b15d734fb6faabe",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=443.0, 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": "1ec87ba9d329480682fa3299c6474062",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1340675298.0, style=ProgressStyle(descr…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-J-x_EHGXJyL"
      },
      "source": [
        "#Import Pytorch\n",
        "import torch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7nHpG3kRucW"
      },
      "source": [
        "#Load Tokenizer \n",
        "#We load from BERT-base uncased \n",
        "\n",
        "from transformers import BertTokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVkXMs9IR4oo"
      },
      "source": [
        "#We can ask a question \n",
        "\n",
        "question = \"How many parameters does BERT-large have?\"\n",
        "answer_text = \"BERT-large is really big... it has 24 layers and an embedding size of 1024 for a total of 340 parameters! Size is 1.34 GB, it takes a couple of minutes\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9XBu7jlSb0J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bb3610e-99de-4203-a7d7-a723bc5a2439"
      },
      "source": [
        "# Both Question and Answer_Text is fed into BERT tokenizer\n",
        "# Concantenate them together and feed them in [SEP] token between them\n",
        "\n",
        "\n",
        "input_ids = tokenizer.encode(question, answer_text)\n",
        "print('The input has a total of {:} tokens.'.format(len(input_ids)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The input has a total of 54 tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bd4U8JMFSwtu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f259b5c3-0f97-4146-b7a8-b70dcbb163f8"
      },
      "source": [
        "#We want to see what the Tokenizer is doing, \n",
        "#We want only IDs from BERT\n",
        "\n",
        "tokens = tokenizer.convert_ids_to_tokens(input_ids) \n",
        "#In Each Token, and the ID\n",
        "\n",
        "for token, id in zip(tokens, input_ids):\n",
        "  #If we find id as [SEP], we add some space around it\n",
        "  if id == tokenizer.sep_token_id:\n",
        "    print('')\n",
        "    #Print token string and its ID in two columns\n",
        "  print('{:<12}{:>6}'.format(token,id))\n",
        "  \n",
        "  if id == tokenizer.sep_token_id: \n",
        "    print('')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[CLS]          101\n",
            "how           2129\n",
            "many          2116\n",
            "parameters   11709\n",
            "does          2515\n",
            "bert         14324\n",
            "-             1011\n",
            "large         2312\n",
            "have          2031\n",
            "?             1029\n",
            "\n",
            "[SEP]          102\n",
            "\n",
            "bert         14324\n",
            "-             1011\n",
            "large         2312\n",
            "is            2003\n",
            "really        2428\n",
            "big           2502\n",
            ".             1012\n",
            ".             1012\n",
            ".             1012\n",
            "it            2009\n",
            "has           2038\n",
            "24            2484\n",
            "layers        9014\n",
            "and           1998\n",
            "an            2019\n",
            "em            7861\n",
            "##bed         8270\n",
            "##ding        4667\n",
            "size          2946\n",
            "of            1997\n",
            "102           9402\n",
            "##4           2549\n",
            "for           2005\n",
            "a             1037\n",
            "total         2561\n",
            "of            1997\n",
            "340          16029\n",
            "parameters   11709\n",
            "!              999\n",
            "size          2946\n",
            "is            2003\n",
            "1             1015\n",
            ".             1012\n",
            "34            4090\n",
            "gb           16351\n",
            ",             1010\n",
            "it            2009\n",
            "takes         3138\n",
            "a             1037\n",
            "couple        3232\n",
            "of            1997\n",
            "minutes       2781\n",
            "\n",
            "[SEP]          102\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-8kvOlAVf-e"
      },
      "source": [
        "#We concantenated them together, BERT needs to distingusih them\n",
        "#BERT has Special Segment embedding, One for Segment A and Segment B\n",
        "#Segment A embedding needs to go into question token\n",
        "#Segment B embedding needs to be added to each of answer_text \n",
        "\n",
        "#Transformer Library handles these with '0' and '1' for each token\n",
        "#We have segment_ids from transformer library\n",
        "\n",
        "#Look into input ids for first instance of '[SEP]' token\n",
        "sep_index = input_ids.index(tokenizer.sep_token_id)\n",
        "\n",
        "#Number of Segment A tokens includes [SEP] token\n",
        "num_seg_a = sep_index + 1\n",
        "\n",
        "#Remaining are Segment B\n",
        "num_seg_b = len(input_ids) - num_seg_a\n",
        "\n",
        "#List of 0's and 1's\n",
        "segment_ids = [0] * num_seg_a + [1] * num_seg_b\n",
        "\n",
        "#Segment_ID for every input token\n",
        "assert len(segment_ids) == len(input_ids)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BrGDIG-WM4u"
      },
      "source": [
        "#Let's run it through our model \n",
        "start_scores, end_scores = model(torch.tensor([input_ids]),\n",
        "                                 token_type_ids=torch.tensor([segment_ids]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwhaqPZlXOHw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ada6882-c671-4cd3-8989-a23c20e4c908"
      },
      "source": [
        "#Find token with highest start and end scores\n",
        "\n",
        "answer_start = torch.argmax(start_scores)\n",
        "answer_end = torch.argmax(end_scores)\n",
        "\n",
        "#Combine tokens in answer and print it out \n",
        "answer = ''.join(tokens[answer_start:answer_end+1])\n",
        "print('Answer:\"'+answer+'\"')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Answer:\"340\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZM9I-9JfXaYy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47451cd6-39ed-42c8-aa1d-c09086140656"
      },
      "source": [
        "#We can reconstruct words that got broken down into subwords \n",
        "\n",
        "answer = tokens[answer_start]\n",
        "\n",
        "#Select remaining answer tokens and join with whitespace\n",
        "for i in range(answer_start+1, answer_end+1):\n",
        "\n",
        "  #If found subword, then recombine it with previous token\n",
        "  if tokens[i][0:2] == '##':\n",
        "    answer += tokens[i][2:]\n",
        "  #If not, add space between token  \n",
        "  else: \n",
        "      answer += '' + tokens[i]\n",
        "print('Answer:\"'+answer+'\"')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Answer:\"340\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9-de_46X_Xi"
      },
      "source": [
        "#Visualizing Scores using Seaborn\n",
        "\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns \n",
        "\n",
        "sns.set(style='darkgrid')\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (16, 8)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2I_Zr4f6YW9W"
      },
      "source": [
        "#We take from Pytorch Tensors and then convert them into 1D numpy arrays\n",
        "s_scores = start_scores.detach().numpy().flatten()\n",
        "e_scores = end_scores.detach().numpy().flatten()\n",
        "\n",
        "#Token as x axis, they'll need to be unique\n",
        "token_labels = [] \n",
        "for (i, token) in enumerate(tokens): token_labels.append('{:} - {:>2}'.format(token, i))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMRbb0u3YuVD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "5cb06f16-4fa8-4b18-a714-48184661cbbc"
      },
      "source": [
        "#Create a Bar plot to show start word score for all tokens\n",
        "ax = sns.barplot(x=token_labels, y=s_scores, ci=None)\n",
        "\n",
        "#Turn xlabels vertical \n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "\n",
        "#Turn on vertical grid to help align words to score\n",
        "ax.grid(True)\n",
        "plt.title(\"start word scores\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWM_bj51Y8gy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "outputId": "885635f5-0033-4e56-85dd-b941bb7d23cc"
      },
      "source": [
        "#Another Bar Plot for scoring end Word\n",
        "ax = sns.barplot(x=token_labels, y=e_scores, ci=None)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "ax.grid(True)\n",
        "plt.title(\"End Word scores\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HWHznNrZK4J"
      },
      "source": [
        "#See both of them together start and end word plotted together\n",
        "import pandas as pd \n",
        "scores = []\n",
        "\n",
        "for (i, token_label) in enumerate(token_labels):\n",
        "  scores.append({'token_label':token_label, 'score':s_scores[i],'marker':'start'})\n",
        "  scores.append({'token_label':token_label, 'score':e_scores[i],'marker':'end'})\n",
        "\n",
        "df = pd.DataFrame(scores)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9hMZPxqCZeJN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "ef8760cc-4c80-4322-f568-3a92988f0c80"
      },
      "source": [
        "g = sns.catplot(x=\"token_label\", y=\"score\", hue=\"marker\",data=df, kind=\"bar\", height=6, aspect=4)\n",
        "g.set_xticklabels(g.ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "g.ax.grid(True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1793.47x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8miL5MC3brT8"
      },
      "source": [
        "# We can create a Function to turn Question Answering into a function\n",
        "\n",
        "\n",
        "def answer_question(question, answer_text):\n",
        "    '''\n",
        "    Takes a `question` string and an `answer_text` string (which contains the\n",
        "    answer), and identifies the words within the `answer_text` that are the\n",
        "    answer. Prints them out.\n",
        "    '''\n",
        "    # ======== Tokenize ========\n",
        "    # Apply the tokenizer to the input text, treating them as a text-pair.\n",
        "    input_ids = tokenizer.encode(question, answer_text)\n",
        "\n",
        "    # Report how long the input sequence is.\n",
        "    print('Query has {:,} tokens.\\n'.format(len(input_ids)))\n",
        "\n",
        "    # ======== Set Segment IDs ========\n",
        "    # Search the input_ids for the first instance of the `[SEP]` token.\n",
        "    sep_index = input_ids.index(tokenizer.sep_token_id)\n",
        "\n",
        "    # The number of segment A tokens includes the [SEP] token istelf.\n",
        "    num_seg_a = sep_index + 1\n",
        "\n",
        "    # The remainder are segment B.\n",
        "    num_seg_b = len(input_ids) - num_seg_a\n",
        "\n",
        "    # Construct the list of 0s and 1s.\n",
        "    segment_ids = [0]*num_seg_a + [1]*num_seg_b\n",
        "\n",
        "    # There should be a segment_id for every input token.\n",
        "    assert len(segment_ids) == len(input_ids)\n",
        "\n",
        "    # ======== Evaluate ========\n",
        "    # Run our example question through the model.\n",
        "    start_scores, end_scores = model(torch.tensor([input_ids]), # The tokens representing our input text.\n",
        "                                    token_type_ids=torch.tensor([segment_ids])) # The segment IDs to differentiate question from answer_text\n",
        "\n",
        "    # ======== Reconstruct Answer ========\n",
        "    # Find the tokens with the highest `start` and `end` scores.\n",
        "    answer_start = torch.argmax(start_scores)\n",
        "    answer_end = torch.argmax(end_scores)\n",
        "\n",
        "    # Get the string versions of the input tokens.\n",
        "    tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
        "\n",
        "    # Start with the first token.\n",
        "    answer = tokens[answer_start]\n",
        "\n",
        "    # Select the remaining answer tokens and join them with whitespace.\n",
        "    for i in range(answer_start + 1, answer_end + 1):\n",
        "        \n",
        "        # If it's a subword token, then recombine it with the previous token.\n",
        "        if tokens[i][0:2] == '##':\n",
        "            answer += tokens[i][2:]\n",
        "        \n",
        "        # Otherwise, add a space then the token.\n",
        "        else:\n",
        "            answer += ' ' + tokens[i]\n",
        "\n",
        "    print('Answer: \"' + answer + '\"')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iv8e9r-pcqOS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b806d3a9-a744-416e-807d-94de47388b9d"
      },
      "source": [
        "#Let's take Abstract of BERT Paper\n",
        "#Text Wrap is used for wrapping and formatting of plain text\n",
        "\n",
        "import textwrap \n",
        "wrapper = textwrap.TextWrapper(width=80)\n",
        "bert_abstract = \"We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).\"\n",
        "\n",
        "print(wrapper.fill(bert_abstract))\n",
        "#Abstract from BERT"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We introduce a new language representation model called BERT, which stands for\n",
            "Bidirectional Encoder Representations from Transformers. Unlike recent language\n",
            "representation models (Peters et al., 2018a; Radford et al., 2018), BERT is\n",
            "designed to pretrain deep bidirectional representations from unlabeled text by\n",
            "jointly conditioning on both left and right context in all layers. As a result,\n",
            "the pre-trained BERT model can be finetuned with just one additional output\n",
            "layer to create state-of-the-art models for a wide range of tasks, such as\n",
            "question answering and language inference, without substantial taskspecific\n",
            "architecture modifications. BERT is conceptually simple and empirically\n",
            "powerful. It obtains new state-of-the-art results on eleven natural language\n",
            "processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute\n",
            "improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1\n",
            "question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD\n",
            "v2.0 Test F1 to 83.1 (5.1 point absolute improvement).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_oS0pKBGc3Dt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3d31090-4a76-4620-c461-3a52a4a99cab"
      },
      "source": [
        "#Let's ask a question in BERT \n",
        "question = \"What does the 'B' in BERT stand for?\"\n",
        "\n",
        "answer_question(question, bert_abstract)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Query has 258 tokens.\n",
            "\n",
            "Answer: \"bidirectional encoder representations from transformers\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FF0_30iPeOjZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXPKYPIweYyY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwQVtIlhefNa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "465c843d-68d9-4b5b-8fe1-30c8e22c942e"
      },
      "source": [
        "#Let's ask BERT about example applications of itself \n",
        "\n",
        "question = \"What are some example applications of BERT?\"\n",
        "answer_question(question, bert_abstract)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Query has 255 tokens.\n",
            "\n",
            "Answer: \"question answering and language inference\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ATcbK5GrRVW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f641d15-dc6d-4bdc-942f-3cabae0b8c87"
      },
      "source": [
        "question = \"How many natural language processing task BERT obtains state-of-the-art results?\"\n",
        "answer_question(question, bert_abstract)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Query has 265 tokens.\n",
            "\n",
            "Answer: \"eleven\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EP7kWaR7rwPl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}