OccCANINE_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/christianvedels/OccCANINE/blob/main/OccCANINE_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJs7nvh2VJ83"
      },
      "source": [
        "# OccCANINE\n",
        "\n",
        "This notebook demonstrates the capabilities of OccCANINE. For any use at scale we recommend looking at [GitHub repository](https://github.com/christianvedels/OccCANINE). A natural starting point is the script [PREDICT_HISCOs.py](https://github.com/christianvedels/OccCANINE/blob/main/PREDICT_HISCOs.py)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setting up everything\n",
        "We start by cloning the GitHub repository"
      ],
      "metadata": {
        "id": "0EVGxJGwZLpg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEk5LgqZZmNu",
        "outputId": "310e05ff-73fa-47d0-e60b-18853e2ae75c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'OccCANINE'...\n",
            "remote: Enumerating objects: 2749, done.\u001b[K\n",
            "remote: Counting objects: 100% (695/695), done.\u001b[K\n",
            "remote: Compressing objects: 100% (257/257), done.\u001b[K\n",
            "remote: Total 2749 (delta 486), reused 581 (delta 433), pack-reused 2054\u001b[K\n",
            "Receiving objects: 100% (2749/2749), 861.95 MiB | 30.15 MiB/s, done.\n",
            "Resolving deltas: 100% (1434/1434), done.\n",
            "Updating files: 100% (541/541), done.\n"
          ]
        }
      ],
      "source": [
        "!rm -rf OccCANINE # Remove existing\n",
        "!git clone https://github.com/christianvedels/OccCANINE"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install histocc package"
      ],
      "metadata": {
        "id": "sAnjU9wI6JUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install OccCANINE/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFOeklAS6II-",
        "outputId": "418f86ef-17e8-4335-8eff-12a3c6cdd1ca"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing ./OccCANINE\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: histocc\n",
            "  Building wheel for histocc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for histocc: filename=histocc-0.1.1-py3-none-any.whl size=351922 sha256=7bdcbcdd7bb8954cd3b1ef7f3e43be8522663f7050f3b47a2f8e9db1759e9211\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-7mzlbcyn/wheels/81/0a/20/821ea5b7ddc47314568c18dee9e67d8401154089f1dcb3fdd2\n",
            "Successfully built histocc\n",
            "Installing collected packages: histocc\n",
            "Successfully installed histocc-0.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1V9R8XM2fK6C"
      },
      "source": [
        "Other required packages. `unidecode` is not standard in colab and needs to be installed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "drDRIxJ9fHWO",
        "outputId": "1897d528-2d9b-4626-c605-42e519f36690"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting unidecode\n",
            "  Downloading Unidecode-1.3.8-py3-none-any.whl (235 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/235.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m \u001b[32m225.3/235.5 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.5/235.5 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: unidecode\n",
            "Successfully installed unidecode-1.3.8\n"
          ]
        }
      ],
      "source": [
        "!pip install unidecode"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We load the 'OccCANINE' class, which is an all-in-one class, which handles loading of the underlying transformer model, prediction and finetuning."
      ],
      "metadata": {
        "id": "Zu0O5wrY6PE1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "TbjmF-_rU633"
      },
      "outputs": [],
      "source": [
        "from histocc import OccCANINE # HISCO prediciton class"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hP4MgdmOfby0"
      },
      "source": [
        "We initialize the model with the class we just loaded"
      ]
    },
    {
      "cell_type": "code",
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/854 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/534M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6e33e21d365429db4b14609b74dd66b"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "pytorch_model.bin:   0%|          | 0.00/529M [00:00<?, ?B/s]"
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              "model_id": "deeb78a3dcbc45a2bd399f6a17d2113f"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "model = OccCANINE() # This downloads and initializes the model from HuggingFace"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example 1\n",
        "*OccCANINE works pretty well out of the box*"
      ],
      "metadata": {
        "id": "i5X1I7p5D4x8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(\n",
        "    [\"tailor of the finest suits\", \"local boiler maker\", \"train's fireman\"],\n",
        "    get_dict = True # Simple output\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vWj-sIA31SF",
        "outputId": "346a230f-5089-4d01-e2b1-b57cf7627055"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[[79190, 0.81692904, 'Other Tailors and Dressmakers']],\n",
              " [[87350, 0.9993395, 'Boilersmith']],\n",
              " [[98330, 0.986886, 'Railway SteamEngine Fireman']]]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example 2\n",
        "***We can get better results by adding language as context***  \n",
        "\n",
        "`lang=\"en\"`\n",
        "\n",
        "- In this case we simply get a higher probability of the correct label.\n",
        "- In other cases, this can make the difference between correct and incorrect\n",
        "- OccCANINE is trained on 13 languages and the following number of observations:\n",
        "  + English: \"en\" (6.34M)\n",
        "  + Danish: \"da\" (4.66M)\n",
        "  + Swedish: \"se\" (1.68M)\n",
        "  + Dutch: \"nl\" (1.00M)\n",
        "  + Catalan: \"ca\" (554K)\n",
        "  + French: \"fr\" (243K)\n",
        "  + Norwegian: \"no\" (136K)\n",
        "  + Icelandic: \"is\" (17.4K)\n",
        "  + Portugese: \"pt\" (17.4K)\n",
        "  + German: \"ge/de\" (11.7k)\n",
        "  + Spanish: \"es\" (7372)\n",
        "  + Italian: \"it\" (3828)\n",
        "  + Greek: \"gr\" (1466)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "vy5ImpbgHVl5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(\n",
        "    [\"tailor of the finest suits\", \"local boiler maker\", \"train's fireman\"],\n",
        "    lang = \"en\",\n",
        "    get_dict = True # Simple output\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKubieQgHVF8",
        "outputId": "b655abbf-e468-41ec-ef0e-62171416be62"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[[79100, 0.8775564, 'Tailor, Specialisation Unknown']],\n",
              " [[87350, 0.99941397, 'Boilersmith']],\n",
              " [[98330, 0.99424595, 'Railway SteamEngine Fireman']]]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fast performance for many observations"
      ],
      "metadata": {
        "id": "fCLO7o-YqMJZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "W-Wk8WZoffQZ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"OccCANINE/histocc/Data/TOYDATA.csv\")\n",
        "model.verbose = True # Set updates to True\n",
        "x = model.predict(\n",
        "    df[\"occ1\"],\n",
        "    lang = \"en\",\n",
        "    threshold = 0.22 # Optimal for f1\n",
        "    )\n",
        "\n",
        "x"
      ],
      "metadata": {
        "id": "uF219PtBqQKP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "outputId": "7809c382-c3c1-40c6-abe8-0372d3fd3af5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed batch 40 out of 40 batches\n",
            "Prediction done. Cleaning results.\n",
            "Produced HISCO codes for 10000 observations in 0 hours, 0 minutes and 27.452 seconds.\n",
            "Estimated hours saved compared to human labeller (assuming 10 seconds per label):\n",
            " ---> 27 hours, 46 minutes and 13 seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 inputs  hisco_1    prob_1  ... hisco_5  prob_5   desc_5\n",
              "0              en[SEP]soldier (reserve)  58340.0  0.999909  ...     NaN     NaN  No pred\n",
              "1       en[SEP]wine and spirit merchant  41025.0  0.999686  ...     NaN     NaN  No pred\n",
              "2       en[SEP]coal merchant (deceased)  41025.0  0.999705  ...     NaN     NaN  No pred\n",
              "3           en[SEP]paper mill operative  99930.0  0.441911  ...     NaN     NaN  No pred\n",
              "4             en[SEP]soldier (deceased)  58340.0  0.999180  ...     NaN     NaN  No pred\n",
              "...                                 ...      ...       ...  ...     ...     ...      ...\n",
              "9995  en[SEP]holloware turner, deceased  83320.0  0.987633  ...     NaN     NaN  No pred\n",
              "9996       en[SEP]construction engineer   2210.0  0.906727  ...     NaN     NaN  No pred\n",
              "9997            en[SEP]operative brewer  77810.0  0.992721  ...     NaN     NaN  No pred\n",
              "9998      en[SEP]clothier and outfitter  41030.0  0.951929  ...     NaN     NaN  No pred\n",
              "9999           en[SEP]sexton (deceased)  55130.0  0.905149  ...     NaN     NaN  No pred\n",
              "\n",
              "[10000 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-33170b67-1327-44cf-84cd-c728ff1feef6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>inputs</th>\n",
              "      <th>hisco_1</th>\n",
              "      <th>prob_1</th>\n",
              "      <th>desc_1</th>\n",
              "      <th>hisco_2</th>\n",
              "      <th>prob_2</th>\n",
              "      <th>desc_2</th>\n",
              "      <th>hisco_3</th>\n",
              "      <th>prob_3</th>\n",
              "      <th>desc_3</th>\n",
              "      <th>hisco_4</th>\n",
              "      <th>prob_4</th>\n",
              "      <th>desc_4</th>\n",
              "      <th>hisco_5</th>\n",
              "      <th>prob_5</th>\n",
              "      <th>desc_5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>en[SEP]soldier (reserve)</td>\n",
              "      <td>58340.0</td>\n",
              "      <td>0.999909</td>\n",
              "      <td>Other Military Ranks</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>en[SEP]wine and spirit merchant</td>\n",
              "      <td>41025.0</td>\n",
              "      <td>0.999686</td>\n",
              "      <td>Working Proprietor (Wholesale or Retail Trade)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>en[SEP]coal merchant (deceased)</td>\n",
              "      <td>41025.0</td>\n",
              "      <td>0.999705</td>\n",
              "      <td>Working Proprietor (Wholesale or Retail Trade)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>en[SEP]paper mill operative</td>\n",
              "      <td>99930.0</td>\n",
              "      <td>0.441911</td>\n",
              "      <td>Factory Worker</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>en[SEP]soldier (deceased)</td>\n",
              "      <td>58340.0</td>\n",
              "      <td>0.999180</td>\n",
              "      <td>Other Military Ranks</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>en[SEP]holloware turner, deceased</td>\n",
              "      <td>83320.0</td>\n",
              "      <td>0.987633</td>\n",
              "      <td>Lathe SetterOperator</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>en[SEP]construction engineer</td>\n",
              "      <td>2210.0</td>\n",
              "      <td>0.906727</td>\n",
              "      <td>Civil Engineer, General</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>en[SEP]operative brewer</td>\n",
              "      <td>77810.0</td>\n",
              "      <td>0.992721</td>\n",
              "      <td>Brewer, General</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>en[SEP]clothier and outfitter</td>\n",
              "      <td>41030.0</td>\n",
              "      <td>0.951929</td>\n",
              "      <td>Working Proprietor (Retail Trade)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>en[SEP]sexton (deceased)</td>\n",
              "      <td>55130.0</td>\n",
              "      <td>0.905149</td>\n",
              "      <td>Janitor</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No pred</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33170b67-1327-44cf-84cd-c728ff1feef6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-33170b67-1327-44cf-84cd-c728ff1feef6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-33170b67-1327-44cf-84cd-c728ff1feef6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f609b74f-e376-4714-a129-eba0626b9b8f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f609b74f-e376-4714-a129-eba0626b9b8f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f609b74f-e376-4714-a129-eba0626b9b8f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"inputs\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4739,\n        \"samples\": [\n          \"en[SEP]station master l.s.w.r\",\n          \"en[SEP]private 8th king's royal irish hussars\",\n          \"en[SEP]engineer / deceased\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hisco_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 339,\n        \"samples\": [\n          98500.0,\n          84900.0,\n          74925.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prob_1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5084,\n        \"samples\": [\n          0.6723445653915405,\n          0.9977770447731018,\n          0.9976466298103333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"desc_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 339,\n        \"samples\": [\n          \"MotorVehicle Drivers\",\n          \"Machinery Fitter (except Electrical), Specialisation Unknown\",\n          \"CoalGas Maker\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hisco_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32216.97561692142,\n        \"min\": -1.0,\n        \"max\": 99999.0,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          83590.0,\n          17140.0,\n          6100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prob_2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 996,\n        \"samples\": [\n          0.42217403650283813,\n          0.36681613326072693,\n          0.2716817259788513\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"desc_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 206,\n        \"samples\": [\n          \"Grain Miller\",\n          \"Railway Engine Driver\",\n          \"Railway and Road Vehicle Loader\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hisco_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31762.39929682437,\n        \"min\": 2210.0,\n        \"max\": 99930.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          39310.0,\n          21220.0,\n          55130.0\n        ],\n        \"semantic_type\": \"\",\n   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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prob_4\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"desc_4\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hisco_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prob_5\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"desc_5\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finetuning\n",
        "OccCANINE works well of the box, but for even better performance, it can be finetuned on for a specific domain with a few observations.\n",
        "\n",
        "- To create data for finetuning OccCANINE can be used for initial predictions, which can be corrected by a human labeller\n",
        "- In turn this can then be used as training data in finetuning\n"
      ],
      "metadata": {
        "id": "80yJ90JVUtKO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup\n",
        "df = pd.read_csv(\n",
        "    \"OccCANINE/histocc/Data/TOYDATA.csv\"\n",
        "    )\n",
        "label_cols = [\"hisco_1\"]\n",
        "\n",
        "# Set lang\n",
        "df[\"lang\"] = \"en\"  # English\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "gcIPncApcVEp",
        "outputId": "9e5af4e8-b9f4-4a07-a61f-3498be8c51bf"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            occ1  hisco_1 lang\n",
              "0              soldier (reserve)    58340   en\n",
              "1       wine and spirit merchant    41025   en\n",
              "2       coal merchant (deceased)    41025   en\n",
              "3           paper mill operative    99930   en\n",
              "4             soldier (deceased)    58340   en\n",
              "...                          ...      ...  ...\n",
              "9995  holloware turner, deceased    83320   en\n",
              "9996       construction engineer     2210   en\n",
              "9997            operative brewer    77810   en\n",
              "9998      clothier and outfitter    41030   en\n",
              "9999           sexton (deceased)    55130   en\n",
              "\n",
              "[10000 rows x 3 columns]"
            ],
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>coal merchant (deceased)</td>\n",
              "      <td>41025</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>paper mill operative</td>\n",
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              "      <td>en</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "    <tr>\n",
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              "      <th>9996</th>\n",
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              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>operative brewer</td>\n",
              "      <td>77810</td>\n",
              "      <td>en</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>clothier and outfitter</td>\n",
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              "      <td>55130</td>\n",
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              "<p>10000 rows × 3 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"occ1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4739,\n        \"samples\": [\n          \"station master l.s.w.r\",\n          \"private 8th king's royal irish hussars\",\n          \"engineer / deceased\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hisco_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30170,\n        \"min\": -1,\n        \"max\": 99999,\n        \"num_unique_values\": 296,\n        \"samples\": [\n          94180,\n          77450,\n          98590\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lang\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"en\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use the .finetune method to finetune the model"
      ],
      "metadata": {
        "id": "2TTPDVJMgvsC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.finetune(\n",
        "    df,\n",
        "    label_cols,\n",
        "    batch_size=64,\n",
        "    save_name = \"Finetune_toy_model\",\n",
        "    verbose_extra = True # Print even more updates while finetuning.\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ej8fc6-xgu9h",
        "outputId": "02e0eef5-7efb-4ec5-e995-bb17e478ffb1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "======================================\n",
            "==== Started finetuning procedure ====\n",
            "======================================\n",
            "9000 observations will be used in training.\n",
            "1000 observations will be used in validation.\n",
            "Saved tmp files to Data/Tmp_finetune\n",
            "----------\n",
            "Intital performance:\n",
            "Validation acc: 0.894140625; Validation loss: 0.0002107825112034334\n",
            "----------\n",
            "Epoch 1/3\n",
            "Batch 141/141 - Loss: 0.0004, Acc: 0.8500, ETA: 0m0s of 1m34s\n",
            "Epoch completed.\n",
            "Train loss 0.00040298335513030677, accuracy 0.8155363475177304\n",
            "Val loss 0.00016636294640193228, accuracy 0.916796875\n",
            "Validation loss improved. Saved improved model\n",
            "----------\n",
            "Epoch 2/3\n",
            "Batch 141/141 - Loss: 0.0002, Acc: 0.9000, ETA: 0m0s of 1m36s\n",
            "Epoch completed.\n",
            "Train loss 0.00030619350757577017, accuracy 0.8596631205673759\n",
            "Val loss 0.00016594656472079805, accuracy 0.912890625\n",
            "Validation loss improved. Saved improved model\n",
            "----------\n",
            "Epoch 3/3\n",
            "Batch 141/141 - Loss: 0.0004, Acc: 0.8500, ETA: 0m0s of 1m36s\n",
            "Epoch completed.\n",
            "Train loss 0.0002664030719559197, accuracy 0.8730496453900709\n",
            "Val loss 0.00016394033627875615, accuracy 0.9173828125\n",
            "Validation loss improved. Saved improved model\n",
            "Loaded best version of model\n",
            "----------\n",
            "Final performance:\n",
            "Validation acc: 0.9173828125; Validation loss: 0.00016394033627875615\n",
            "Finetuning completed successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "*   The finetuned model is saved in a new folder called \"Finetuned\"\n",
        "*   It can be loaded in other projects with the OccCANINE class\n",
        "\n"
      ],
      "metadata": {
        "id": "fmFoxYg6NMc0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_new = OccCANINE(\"Finetuned/Finetune_toy_model\", hf = False)\n",
        "model_new.predict(\n",
        "    [\"tailor of the finest suits\", \"local boiler maker\", \"train's fireman\"],\n",
        "    lang = \"en\",\n",
        "    get_dict = True # Simple output\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_EcmneWNvpv",
        "outputId": "b42e237b-edfe-490b-b9b0-e81c20a2c1c4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[[79100, 0.94281733, 'Tailor, Specialisation Unknown']],\n",
              " [[87350, 0.9936194, 'Boilersmith']],\n",
              " [[98330, 0.97705656, 'Railway SteamEngine Fireman']]]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    }
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