{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "14r06gMaaaxk"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datasets import DownloadConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "download_config = DownloadConfig(delete_extracted=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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     ]
    },
    "id": "8Sod42WWadRA",
    "outputId": "96b6fb42-9c04-42c3-bad9-e7a588304162"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-0663d2a6043864fc\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /Users/dvanstrien/.cache/huggingface/datasets/csv/default-0663d2a6043864fc/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebd9db43605f42798bca44a5866629b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af406ad5026f45748f85fb2dd05ab4a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f576e1da3c8442da89bde6180aaa8521",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 tables [00:00, ? tables/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /Users/dvanstrien/.cache/huggingface/datasets/csv/default-0663d2a6043864fc/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "183e9cdd2c184dfb97a9d08a02194853",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = load_dataset(\"csv\", \n",
    "                  data_files=\"https://zenodo.org/record/3257041/files/xml2csv_alto.csv?download=1\", \n",
    "                  download_config=download_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "EdXeGNdlTnQa"
   },
   "outputs": [],
   "source": [
    "def parse_wc(example):\n",
    "    if wc := example['wc']:\n",
    "        wc = wc.split(\" \")\n",
    "        wc = [float(x) for x in wc]\n",
    "        return {\"wc\": wc}\n",
    "    else:\n",
    "        return  {\"wc\":[] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200,
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    },
    "id": "Vu3oBUAzO5BI",
    "outputId": "b8376629-7d5c-408b-85ea-c34ca3947ba0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter 'function'=<function parse_wc at 0x15eaa83a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae01361fd9cd432088b9f12a4dbcc614",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#0:   0%|          | 0/1247025 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56a1ae63cc6d463a97703700fe14a458",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#2:   0%|          | 0/1247025 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e80b242e4c8419bbc9e1509048c2c9d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#3:   0%|          | 0/1247024 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b948a1ccbbc646b39195810393b7ea9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#1:   0%|          | 0/1247025 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = ds.map(parse_wc ,num_proc=4, writer_batch_size=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FnbPNZn6PuSA"
   },
   "outputs": [],
   "source": [
    "!wget -O corpus-language.pkl https://zenodo.org/record/3257041/files/corpus-language.pkl?download=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "D-BVccuCPxw7"
   },
   "outputs": [],
   "source": [
    "with open(\"corpus-language.pkl\",\"rb\") as f:\n",
    "    data = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "XqSrCg4GcsEs"
   },
   "outputs": [
    {
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       "               ppn      filename language  confidence\n",
       "0     PPN646426230  00000045.xml       fr         1.0\n",
       "1     PPN646426230  00000218.xml       fr         1.0\n",
       "2     PPN646426230  00000394.xml       fr         1.0\n",
       "3     PPN646426230  00000071.xml       fr         1.0\n",
       "4     PPN646426230  00000317.xml       fr         1.0\n",
       "...            ...           ...      ...         ...\n",
       "7479  PPN821899872  00000017.xml       de         1.0\n",
       "7480  PPN821899872  00000510.xml       de         1.0\n",
       "7481  PPN821899872  00000487.xml       de         1.0\n",
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       "\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['in'] = data['ppn'] + \"_\" + data['filename']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop_duplicates(subset=['in'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Pt4H7_8wPuOz"
   },
   "outputs": [],
   "source": [
    "data = data.set_index(\"in\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>fr</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPN646426230_00000394.xml</th>\n",
       "      <td>PPN646426230</td>\n",
       "      <td>00000394.xml</td>\n",
       "      <td>fr</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPN646426230_00000071.xml</th>\n",
       "      <td>PPN646426230</td>\n",
       "      <td>00000071.xml</td>\n",
       "      <td>fr</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPN646426230_00000317.xml</th>\n",
       "      <td>PPN646426230</td>\n",
       "      <td>00000317.xml</td>\n",
       "      <td>fr</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPN821899872_00000017.xml</th>\n",
       "      <td>PPN821899872</td>\n",
       "      <td>00000017.xml</td>\n",
       "      <td>de</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPN821899872_00000510.xml</th>\n",
       "      <td>PPN821899872</td>\n",
       "      <td>00000510.xml</td>\n",
       "      <td>de</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPN821899872_00000487.xml</th>\n",
       "      <td>PPN821899872</td>\n",
       "      <td>00000487.xml</td>\n",
       "      <td>de</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPN821899872_00000187.xml</th>\n",
       "      <td>PPN821899872</td>\n",
       "      <td>00000187.xml</td>\n",
       "      <td>de</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPN821899872_00000274.xml</th>\n",
       "      <td>PPN821899872</td>\n",
       "      <td>00000274.xml</td>\n",
       "      <td>de</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4697574 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    ppn      filename language  confidence\n",
       "in                                                                        \n",
       "PPN646426230_00000045.xml  PPN646426230  00000045.xml       fr         1.0\n",
       "PPN646426230_00000218.xml  PPN646426230  00000218.xml       fr         1.0\n",
       "PPN646426230_00000394.xml  PPN646426230  00000394.xml       fr         1.0\n",
       "PPN646426230_00000071.xml  PPN646426230  00000071.xml       fr         1.0\n",
       "PPN646426230_00000317.xml  PPN646426230  00000317.xml       fr         1.0\n",
       "...                                 ...           ...      ...         ...\n",
       "PPN821899872_00000017.xml  PPN821899872  00000017.xml       de         1.0\n",
       "PPN821899872_00000510.xml  PPN821899872  00000510.xml       de         1.0\n",
       "PPN821899872_00000487.xml  PPN821899872  00000487.xml       de         1.0\n",
       "PPN821899872_00000187.xml  PPN821899872  00000187.xml       de         1.0\n",
       "PPN821899872_00000274.xml  PPN821899872  00000274.xml       de         1.0\n",
       "\n",
       "[4697574 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_dict = data.to_dict('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lookup_lang_confidence(example):\n",
    "    ppn = example['ppn']\n",
    "    fname = example['file name']\n",
    "    if match := lookup_dict.get(f\"PPN{ppn}_{fname}\"):\n",
    "        return {\"language\": match['language'],'language_confidence': match['confidence']}\n",
    "    else:\n",
    "        return {\"language\": None,\"language_confidence\": None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'language': 'fr', 'language_confidence': 1.0}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup_lang_confidence(ds['train'][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "ab7308a1b84146a18d74f603d100b2dd",
      "968838b1ba144df1ae7bb34279303e57",
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      "8f42042ef34f4649b2c97a99140812a6",
      "5e22fa51c8b54c39a306a34c4a354b16",
      "98d2ef57a9f84bf3ab43fb0be936f6c1",
      "2bbb6c575f594c968e1c7db67d6b6c9a",
      "3a6cbc51c3324e5ab981b2e82b8ec0d5",
      "5c572b39b21b46bfa25e386c564869b9",
      "4655df7d1d7548e08bf823708377dc88",
      "b49d3d9226ba4436ba301ff9ebe3b791"
     ]
    },
    "id": "Arr7ArLcdu2h",
    "outputId": "caf00be0-8351-4e1f-fb97-4c56692a6758"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f6c519d0bfd4bf9b013b33785a68088",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#0:   0%|          | 0/1247025 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4f2f26388d04cc3a1037b54a80670ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#1:   0%|          | 0/1247025 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff2da1bd43fb4b8a9687eb3860cc796d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#2:   0%|          | 0/1247025 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c3c8fae932e4eea8eee37cf283b42b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#3:   0%|          | 0/1247024 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = ds.map(lookup_lang_confidence, num_proc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['file name', 'text', 'wc', 'ppn', 'language', 'language_confidence'],\n",
       "        num_rows: 4988099\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`analyticaldf.csv` was generated using: https://github.com/elektrobohemian/StabiHacks/tree/master/oai-analyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_df = pd.read_csv(\"data_prep_code/analysis/analyticaldf.csv\",\n",
    " sep=\";\",\n",
    " low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['publisher', 'place', 'date', 'title', 'subTitle', 'language', 'aut',\n",
       "       'rcp', 'fnd', 'access', 'altoPaths', 'asn', 'ppn', 'oth', 'scr', 'wpr',\n",
       "       'pbl', 'prt', 'edt', 'ctb', 'dst', 'cmp', 'dte', 'hnr', 'trl', 'wst',\n",
       "       'art', 'orm', 'isb', 'com', 'pht', 'dto', 'dgg', 'egr', 'ill', 'aft',\n",
       "       'jug', 'pra', 'rsp', 'ltg', 'arr', 'plt', 'dfd', 'cll', 'lyr', 'cre',\n",
       "       'wac', 'cns', 'his', 'tra', 'lbt', 'win', 'spk', 'aui', 'wat', 'ant',\n",
       "       'Unnamed: 56', 'partName', 'chr', 'trc', 'ctg', 'bkd', 'wal', 'dsr',\n",
       "       'prm', 'cmm', 'col', 'mfr', 'enj'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df = metadata_df.replace(np.nan, None)\n",
    "metadata_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_df = metadata_df.loc[:,['publisher','place','date','title','aut','ppn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publisher</th>\n",
       "      <th>place</th>\n",
       "      <th>date</th>\n",
       "      <th>title</th>\n",
       "      <th>aut</th>\n",
       "      <th>ppn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hampelius</td>\n",
       "      <td>Giessae Hessorum</td>\n",
       "      <td>1620</td>\n",
       "      <td>Collegium Ethicum, In Quo De Summo Hominis Bon...</td>\n",
       "      <td>Liebenthal, Christianus</td>\n",
       "      <td>PPN1000000974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Egenolphus</td>\n",
       "      <td>Marpurgi</td>\n",
       "      <td>1614</td>\n",
       "      <td>De Auguriis</td>\n",
       "      <td>Niphus, Augustinus</td>\n",
       "      <td>PPN100000127X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Breitkopf &amp; Härtel</td>\n",
       "      <td>Leipsic</td>\n",
       "      <td>1806</td>\n",
       "      <td>Concerto pour la Flûte avec Accompagnement de ...</td>\n",
       "      <td>Danzi, Franz</td>\n",
       "      <td>PPN1000006808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Königliche privil. Buchdruckerey</td>\n",
       "      <td>Glückstadt</td>\n",
       "      <td>1719</td>\n",
       "      <td>Schuldige Condolentz-Zeilen Womit seinem Gelie...</td>\n",
       "      <td>Koltemann, Otto Benedict</td>\n",
       "      <td>PPN1000056597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oehmigke</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>1796</td>\n",
       "      <td>Vorschlag zur Verbesserung des doppelten Leist...</td>\n",
       "      <td>Böttcher, Johann Friedrich</td>\n",
       "      <td>PPN1000059669</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121690</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1856</td>\n",
       "      <td>Der Wächter</td>\n",
       "      <td>None</td>\n",
       "      <td>PPN74709716X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121691</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1857</td>\n",
       "      <td>Der Wächter</td>\n",
       "      <td>None</td>\n",
       "      <td>PPN747097208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121692</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1858</td>\n",
       "      <td>Der Wächter</td>\n",
       "      <td>None</td>\n",
       "      <td>PPN747097224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121693</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1859</td>\n",
       "      <td>Der Wächter</td>\n",
       "      <td>None</td>\n",
       "      <td>PPN747097240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121694</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1860</td>\n",
       "      <td>Der Wächter</td>\n",
       "      <td>None</td>\n",
       "      <td>PPN747097267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121695 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               publisher             place  date  \\\n",
       "0                              Hampelius  Giessae Hessorum  1620   \n",
       "1                             Egenolphus          Marpurgi  1614   \n",
       "2                     Breitkopf & Härtel           Leipsic  1806   \n",
       "3       Königliche privil. Buchdruckerey        Glückstadt  1719   \n",
       "4                               Oehmigke            Berlin  1796   \n",
       "...                                  ...               ...   ...   \n",
       "121690                              None              None  1856   \n",
       "121691                              None              None  1857   \n",
       "121692                              None              None  1858   \n",
       "121693                              None              None  1859   \n",
       "121694                              None              None  1860   \n",
       "\n",
       "                                                    title  \\\n",
       "0       Collegium Ethicum, In Quo De Summo Hominis Bon...   \n",
       "1                                             De Auguriis   \n",
       "2       Concerto pour la Flûte avec Accompagnement de ...   \n",
       "3       Schuldige Condolentz-Zeilen Womit seinem Gelie...   \n",
       "4       Vorschlag zur Verbesserung des doppelten Leist...   \n",
       "...                                                   ...   \n",
       "121690                                        Der Wächter   \n",
       "121691                                        Der Wächter   \n",
       "121692                                        Der Wächter   \n",
       "121693                                        Der Wächter   \n",
       "121694                                        Der Wächter   \n",
       "\n",
       "                               aut            ppn  \n",
       "0          Liebenthal, Christianus  PPN1000000974  \n",
       "1               Niphus, Augustinus  PPN100000127X  \n",
       "2                     Danzi, Franz  PPN1000006808  \n",
       "3         Koltemann, Otto Benedict  PPN1000056597  \n",
       "4       Böttcher, Johann Friedrich  PPN1000059669  \n",
       "...                            ...            ...  \n",
       "121690                        None   PPN74709716X  \n",
       "121691                        None   PPN747097208  \n",
       "121692                        None   PPN747097224  \n",
       "121693                        None   PPN747097240  \n",
       "121694                        None   PPN747097267  \n",
       "\n",
       "[121695 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_df = metadata_df.drop_duplicates(subset=['ppn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_dict = metadata_df.set_index('ppn').to_dict('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['publisher', 'place', 'date', 'title', 'aut'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup_dict['PPN1000000974'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lookup_metadata(example):\n",
    "    ppn = example['ppn']\n",
    "    if match := lookup_dict.get(f\"PPN{ppn}\"):\n",
    "        return {\"publisher\": match['publisher'],'place': match['place'], 'date': match['date'], 'title': match['title'], 'aut': match['aut']}\n",
    "    else:\n",
    "        return {\"publisher\": None,\"place\": None, \"date\": None, \"title\": None, \"aut\": None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'publisher': 'Imprimerie de la Mission Catholique',\n",
       " 'place': 'Chang-hai',\n",
       " 'date': '1912',\n",
       " 'title': 'Les pratiques superstitieuses',\n",
       " 'aut': 'Doré, Henri'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup_metadata(ds['train'][100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b3aaea327a043379e090cd99e90eb53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "#1:   0%|          | 0/1247025 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51c364b5c9f94789b9834f1d49fb6261",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4763aec758bf4cef943fdd4aa9128c1e",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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