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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3ab2e823-50c9-40d4-9401-3ed7869da6e2",
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "241ea0f7-02bf-4a3e-845c-e262b1d32031",
"metadata": {},
"outputs": [],
"source": [
"# Use specific revision for reproducibility!\n",
"# See https://huggingface.co/datasets/avramandrei/histnero\n",
"revision = \"433ca166efac28c952813c0e78bf301643cf5af3\"\n",
"\n",
"ds = load_dataset(\"avramandrei/histnero\", revision=revision)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "66878e9e-83e8-4010-b81c-cefbc2ef0da7",
"metadata": {},
"outputs": [],
"source": [
"# We are grouping together documents together first!\n",
"def perform_document_grouping(dataset_split):\n",
" # Document identifier -> Training example\n",
" document_mapping = {}\n",
"\n",
" for document in dataset_split:\n",
" doc_id = document[\"doc_id\"]\n",
" if doc_id in document_mapping:\n",
" document_mapping[doc_id].append(document)\n",
" else:\n",
" document_mapping[doc_id] = [document]\n",
" return document_mapping\n",
"\n",
"def export_to_conll(grouped_dataset_split, export_filename):\n",
" dataset_labels = ds[\"train\"].features[\"ner_tags\"].feature.names\n",
" dataset_label_id_to_string = {idx: label_string for idx, label_string in enumerate(dataset_labels)}\n",
"\n",
" with open(export_filename, \"wt\") as f_out:\n",
" for document_name, training_examples in grouped_dataset_split.items():\n",
" f_out.write(\"-DOCSTART-\\tO\\n\\n\")\n",
"\n",
" for training_example in training_examples:\n",
" tokens = training_example[\"tokens\"]\n",
" ner_label_ids = training_example[\"ner_tags\"]\n",
" ner_label_iobs = [dataset_label_id_to_string[ner_label_id] for ner_label_id in ner_label_ids]\n",
"\n",
" assert len(tokens) == len(ner_label_iobs)\n",
"\n",
" # Write some metadata first\n",
" metadata = [\n",
" {\"id\": training_example[\"id\"]},\n",
" {\"doc_id\": training_example[\"doc_id\"]},\n",
" {\"region\": training_example[\"region\"]},\n",
" ]\n",
"\n",
" for metadata_entry in metadata:\n",
" for metadata_name, metadata_value in metadata_entry.items():\n",
" f_out.write(f\"# histnero:{metadata_name} = {metadata_value}\\n\")\n",
" \n",
" for token, ner_label_iob in zip(tokens, ner_label_iobs):\n",
" f_out.write(f\"{token}\\t{ner_label_iob}\\n\")\n",
"\n",
" f_out.write(\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "afb1dc77-1cde-43d5-9d9a-e7b458c08bb5",
"metadata": {},
"outputs": [],
"source": [
"for dataset_split in [\"train\", \"valid\", \"test\"]:\n",
" grouped_dataset = perform_document_grouping(ds[dataset_split])\n",
"\n",
" split_filename = \"dev\" if dataset_split == \"valid\" else dataset_split\n",
" export_to_conll(grouped_dataset, f\"{split_filename}.tsv\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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