Name_Entity_Recognition_Tutorial_SPACY.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNxpkXenmWw323a4RbtUhuI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ludwigwittgenstein2/Research/blob/master/Name_Entity_Recognition_Tutorial_SPACY.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Name Entity Recognition using BERT model"
      ],
      "metadata": {
        "id": "X63uWyUiNR5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -U spacy -q"
      ],
      "metadata": {
        "id": "l7-ZlPjbOzJn"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "at6y_g9iT6Ft",
        "outputId": "bf1dbee0-89f5-45f9-9c18-69c140151e5b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.12.1+cu113)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (4.1.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install spacy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCgFXHA7UdNs",
        "outputId": "a943e3c6-9187-4a13-d8ee-18a5cffe01af"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (3.4.3)\n",
            "Requirement already satisfied: typing-extensions<4.2.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy) (4.1.1)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.10.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.11.3)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.9)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.0.7)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy) (57.4.0)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.10 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.0.10)\n",
            "Requirement already satisfied: typer<0.8.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.4.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (4.64.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.21.6)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.0.8)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.23.0)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (21.3)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.3.0)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.3)\n",
            "Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.6.2)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.0.8)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (8.1.5)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.10.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy) (3.10.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy) (3.0.9)\n",
            "Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy) (5.2.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2022.9.24)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\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<3.0.0,>=2.13.0->spacy) (1.24.3)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy) (0.0.3)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy) (0.7.9)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.8.0,>=0.3.0->spacy) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy) (2.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3HLOivrNVCu",
        "outputId": "9141f90f-8191-40a2-c5a5-dcc7ad876041"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-11-14 18:27:11.648067: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
            "\u001b[1m\n",
            "============================== Info about spaCy ==============================\u001b[0m\n",
            "\n",
            "spaCy version    3.4.3                         \n",
            "Location         /usr/local/lib/python3.7/dist-packages/spacy\n",
            "Platform         Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic\n",
            "Python version   3.7.15                        \n",
            "Pipelines        en_core_web_lg (3.4.1), en_core_web_sm (3.4.1)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "from spacy.tokens import DocBin\n",
        "from tqdm import tqdm\n",
        "\n",
        "nlp = spacy.blank(\"en\") # load a new spacy model\n",
        "db = DocBin() # create a DocBin object"
      ],
      "metadata": {
        "id": "fiOsPsBMVxAr"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "f = open('training_data.json')\n",
        "TRAIN_DATA = json.load(f)"
      ],
      "metadata": {
        "id": "arnG4jIiV27G"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for text, annot in tqdm(TRAIN_DATA['annotations']): \n",
        "    doc = nlp.make_doc(text) \n",
        "    ents = []\n",
        "    for start, end, label in annot[\"entities\"]:\n",
        "        span = doc.char_span(start, end, label=label, alignment_mode=\"contract\")\n",
        "        if span is None:\n",
        "            print(\"Skipping entity\")\n",
        "        else:\n",
        "            ents.append(span)\n",
        "    doc.ents = ents \n",
        "    db.add(doc)\n",
        "\n",
        "db.to_disk(\"./training_data.spacy\") # save the docbin object"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ach9PqEV5UN",
        "outputId": "20819f7b-c47e-4379-f137-11c37ecfbe1f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:00<00:00, 786.82it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget https://raw.githubusercontent.com/amrrs/custom-ner-with-spacy/main/pvr_training_data.json"
      ],
      "metadata": {
        "id": "HCdnV5r7OPBp"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "f = open('training_data.json')\n",
        "TRAIN_DATA = json.load(f)"
      ],
      "metadata": {
        "id": "6t-IDTH7OS4h"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import spacy"
      ],
      "metadata": {
        "id": "pgNxXM5uUBmB"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class NerDataset(torch.utils.data.Dataset):\n",
        "  \"\"\"\n",
        "  Custom dataset implementation to get (text,labels) tuples\n",
        "  Inputs:\n",
        "   - df : dataframe with columns [tags, sentence]\n",
        "  \"\"\"\n",
        "  \n",
        "  def __init__(self, df):\n",
        "    if not isinstance(df, pd.DataFrame):\n",
        "      raise TypeError('Input should be a dataframe')\n",
        "    \n",
        "    if \"tags\" not in df.columns or \"sentence\" not in df.columns:\n",
        "      raise ValueError(\"Dataframe should contain 'tags' and 'sentence' columns\")\n",
        "\n",
        "     \n",
        "    \n",
        "    tags_list = [i.split() for i in df[\"tags\"].values.tolist()]\n",
        "    texts = df[\"sentence\"].values.tolist()\n",
        "\n",
        "    self.texts = [tokenizer(text, padding = \"max_length\", truncation = True, return_tensors = \"pt\") for text in texts]\n",
        "    self.labels = [match_tokens_labels(text, tags) for text,tags in zip(self.texts, tags_list)]\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.labels)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    batch_text = self.texts[idx]\n",
        "    batch_labels = self.labels[idx]\n",
        "\n",
        "    return batch_text, torch.LongTensor(batch_labels)"
      ],
      "metadata": {
        "id": "QXd6QJnxOVa-"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! python -m spacy init config config.cfg --lang en --pipeline ner --optimize efficiency"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1KFT3iXUJHR",
        "outputId": "1719a996-2ad3-4cb8-b284-008a98177918"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-11-14 18:27:46.212089: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
            "\n",
            "\u001b[38;5;1m✘ The provided output file already exists. To force overwriting the\n",
            "config file, set the --force or -F flag.\u001b[0m\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy train config.cfg --output ./ --paths.train ./training_data.spacy --paths.dev ./training_data.spacy\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bv7YZSLpULyE",
        "outputId": "b0c0ecfd-5f12-468f-b158-879e94d993f8"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-11-14 18:27:54.336138: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
            "\u001b[38;5;4mℹ Saving to output directory: .\u001b[0m\n",
            "\u001b[38;5;4mℹ Using CPU\u001b[0m\n",
            "\u001b[1m\n",
            "=========================== Initializing pipeline ===========================\u001b[0m\n",
            "[2022-11-14 18:27:55,354] [INFO] Set up nlp object from config\n",
            "INFO:spacy:Set up nlp object from config\n",
            "[2022-11-14 18:27:55,368] [INFO] Pipeline: ['tok2vec', 'ner']\n",
            "INFO:spacy:Pipeline: ['tok2vec', 'ner']\n",
            "[2022-11-14 18:27:55,375] [INFO] Created vocabulary\n",
            "INFO:spacy:Created vocabulary\n",
            "[2022-11-14 18:27:55,377] [INFO] Finished initializing nlp object\n",
            "INFO:spacy:Finished initializing nlp object\n",
            "[2022-11-14 18:27:55,557] [INFO] Initialized pipeline components: ['tok2vec', 'ner']\n",
            "INFO:spacy:Initialized pipeline components: ['tok2vec', 'ner']\n",
            "\u001b[38;5;2m✔ Initialized pipeline\u001b[0m\n",
            "\u001b[1m\n",
            "============================= Training pipeline =============================\u001b[0m\n",
            "\u001b[38;5;4mℹ Pipeline: ['tok2vec', 'ner']\u001b[0m\n",
            "\u001b[38;5;4mℹ Initial learn rate: 0.001\u001b[0m\n",
            "E    #       LOSS TOK2VEC  LOSS NER  ENTS_F  ENTS_P  ENTS_R  SCORE \n",
            "---  ------  ------------  --------  ------  ------  ------  ------\n",
            "  0       0          0.00     44.11    0.00    0.00    0.00    0.00\n",
            " 80     200        202.23   2302.12  100.00  100.00  100.00    1.00\n",
            "180     400          0.00      0.00  100.00  100.00  100.00    1.00\n",
            "280     600          0.00      0.00  100.00  100.00  100.00    1.00\n",
            "435     800          0.00      0.00  100.00  100.00  100.00    1.00\n",
            "635    1000          0.00      0.00  100.00  100.00  100.00    1.00\n",
            "835    1200          0.00      0.00  100.00  100.00  100.00    1.00\n",
            "1035    1400          0.00      0.00  100.00  100.00  100.00    1.00\n",
            "1235    1600          0.00      0.00  100.00  100.00  100.00    1.00\n",
            "1435    1800          0.00      0.00  100.00  100.00  100.00    1.00\n",
            "\u001b[38;5;2m✔ Saved pipeline to output directory\u001b[0m\n",
            "model-last\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy.cli\n",
        "spacy.cli.download(\"en_core_web_lg\")\n",
        "nlp = spacy.load(\"en_core_web_lg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQEISuD3UWNC",
        "outputId": "656681e5-a8a1-43f2-b58c-8b77d13f4223"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_lg')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp_ner = spacy.load(\"/content/model-best\")"
      ],
      "metadata": {
        "id": "z1zFlEpiUPO8"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc = nlp_ner('Mr. Breonia Adam , PhD, QC, \"\t\"513 LANDWYCK LN, FLOWER MOUND ,TX 75028') # input sample text"
      ],
      "metadata": {
        "id": "2iiMSFTCUNRH"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spacy.displacy.render(doc, style=\"ent\", jupyter=True) # display in Jupyter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "VGLrLhtKWiXw",
        "outputId": "ac736227-b2c7-46a3-a70b-fbb99ed3956a"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
              "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    Mr. Breonia Adam , PhD, QC\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">NAME</span>\n",
              "</mark>\n",
              ", &quot;\t\n",
              "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    &quot;513 LANDWYCK LN, FLOWER MOUND\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ADDRESS</span>\n",
              "</mark>\n",
              " ,\n",
              "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    TX\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">STATE</span>\n",
              "</mark>\n",
              " \n",
              "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    75028\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">NUMBER</span>\n",
              "</mark>\n",
              "</div></span>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}