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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-10 12:21:50.698143: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-05-10 12:21:50.701405: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-05-10 12:21:50.745330: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-05-10 12:21:51.413922: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import flair\n",
    "\n",
    "from flair.datasets.sequence_labeling import ColumnCorpus\n",
    "from flair.file_utils import cached_path\n",
    "\n",
    "from pathlib import Path\n",
    "from typing import Optional, Union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ddcffce-b914-4250-848a-e8cb5b9cc049",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NER_HISTNERO(ColumnCorpus):\n",
    "    def __init__(\n",
    "        self,\n",
    "        base_path: Optional[Union[str, Path]] = None,\n",
    "        in_memory: bool = True,\n",
    "        **corpusargs,\n",
    "    ) -> None:\n",
    "        base_path = flair.cache_root / \"datasets\" if not base_path else Path(base_path)\n",
    "        dataset_name = self.__class__.__name__.lower()\n",
    "        data_folder = base_path / dataset_name\n",
    "        data_path = flair.cache_root / \"datasets\" / dataset_name\n",
    "\n",
    "        column_format = {0: \"text\", 1: \"ner\"}\n",
    "\n",
    "        hf_download_path = \"https://huggingface.co/datasets/stefan-it/histnero/resolve/main\"\n",
    "\n",
    "        for split in [\"train\", \"dev\", \"test\"]:\n",
    "            cached_path(f\"{hf_download_path}/{split}.tsv\", data_path)\n",
    "        \n",
    "        super().__init__(\n",
    "            data_folder,\n",
    "            column_format = {0: \"text\", 1: \"ner\"},\n",
    "            column_delimiter=\"\\t\",\n",
    "            document_separator_token=\"-DOCSTART-\",\n",
    "            in_memory=in_memory,\n",
    "            comment_symbol=\"# \",\n",
    "            **corpusargs,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "caf404fb-b4f1-4688-81ef-abbd950beb0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-05-10 12:21:56,117 Reading data from /home/stefan/.flair/datasets/ner_histnero\n",
      "2024-05-10 12:21:56,118 Train: /home/stefan/.flair/datasets/ner_histnero/train.tsv\n",
      "2024-05-10 12:21:56,119 Dev: /home/stefan/.flair/datasets/ner_histnero/dev.tsv\n",
      "2024-05-10 12:21:56,120 Test: /home/stefan/.flair/datasets/ner_histnero/test.tsv\n"
     ]
    }
   ],
   "source": [
    "corpus = NER_HISTNERO()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c38074b8-36c6-47f8-9a9c-b9685514e4bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corpus: 8149 train + 1114 dev + 1117 test sentences\n"
     ]
    }
   ],
   "source": [
    "print(str(corpus))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1bfac4-d09f-477a-b1d8-0543d2e4368c",
   "metadata": {},
   "source": [
    "# Tests\n",
    "\n",
    "We now check the number of parsed sentences and compare with the reference values from the HistNERo paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f1dc9c90-c14c-44c6-9716-5e341c73d370",
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_training_split_sentences = {\n",
    "    \"train\": 8_020,\n",
    "    \"dev\": 1_003,\n",
    "    \"test\": 1_003,\n",
    "}\n",
    "\n",
    "flair_corpus_mapping = {\n",
    "    \"train\": corpus.train,\n",
    "    \"dev\": corpus.dev,\n",
    "    \"test\": corpus.test,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "957921b4-53f6-4c49-a916-339ff0296eee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔️ Number of parsed sentences for train split\n",
      "✔️ Number of parsed sentences for dev split\n",
      "✔️ Number of parsed sentences for test split\n",
      "✔️ Number of parsed sentences for complete dataset\n"
     ]
    }
   ],
   "source": [
    "actual_total_sentences = 0\n",
    "gold_total_sentences = sum(gold_training_split_sentences.values())\n",
    "\n",
    "for dataset_split in [\"train\", \"dev\", \"test\"]:\n",
    "    gold_sentences = gold_training_split_sentences[dataset_split]\n",
    "\n",
    "    actual_sentences = 0\n",
    "    \n",
    "    for sentence in flair_corpus_mapping[dataset_split]:\n",
    "        # We do not count document marker as sentences!\n",
    "        if sentence[0].text.startswith(\"-DOCSTART-\"):\n",
    "            continue\n",
    "        actual_sentences += 1\n",
    "\n",
    "    actual_total_sentences += actual_sentences\n",
    "\n",
    "    assert gold_sentences == actual_sentences, f\"Mismatch of parsed sentences for {dataset_split} split!\"\n",
    "\n",
    "    print(f\"✔️ Number of parsed sentences for {dataset_split} split\")\n",
    "\n",
    "assert actual_total_sentences == gold_total_sentences, f\"Mismatch in total parsed sentences!\"\n",
    "\n",
    "print(f\"✔️ Number of parsed sentences for complete dataset\")"
   ]
  }
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