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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "7301572a-4803-4a16-b262-74e41e25803e",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"import pandas as pd\n",
"from datasets import Dataset, DatasetDict, load_dataset\n",
"from huggingface_hub import Repository, create_repo\n",
"from selectolax.parser import HTMLParser"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a898baed-3640-4ca8-9d3d-b88f6c85a428",
"metadata": {},
"outputs": [],
"source": [
"def _parse_start_end(node):\n",
" return int(node.attrs[\"start\"][1:]), int(node.attrs[\"end\"][1:])\n",
"\n",
"\n",
"def get_original_text(sent_toks) -> str:\n",
" empty_tokens = [i for i, t in enumerate(sent_toks) if not t.text().strip()]\n",
" org_sent_toks = [t.text() for i, t in enumerate(sent_toks) if not i in empty_tokens]\n",
" return \" \".join(org_sent_toks)\n",
"\n",
"\n",
"def get_corrected_text(toks_cor, last_end, sent_end) -> str:\n",
" cor_toks = []\n",
" for tok in toks_cor:\n",
" tok_start, tok_end = _parse_start_end(tok)\n",
" if tok_start >= last_end and tok_end <= sent_end:\n",
" cor_toks.append(tok.text())\n",
" last_end = tok_end\n",
" return last_end, \" \".join(cor_toks)\n",
"\n",
"\n",
"def process_doc(doc, path):\n",
" toks = doc.select('tier[category=\"tok\"] event').matches\n",
" toks_cor = doc.select('tier[category=\"TH1\"] event').matches\n",
" sents = doc.select('tier[category=\"sentence\"] event').matches\n",
"\n",
" last_end = 0\n",
" for sent_no, org_sent in enumerate(sents):\n",
" sent_start, sent_end = _parse_start_end(org_sent)\n",
" sent_toks = toks[sent_start:sent_end]\n",
" original_text = get_original_text(sent_toks)\n",
" last_end, corrected_text = get_corrected_text(toks_cor, last_end, sent_end)\n",
"\n",
" yield (\n",
" {\n",
" \"original\": original_text,\n",
" \"corrected\": corrected_text,\n",
" \"id\": f\"{path.stem}-{sent_no}\",\n",
" }\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "191fe2e3-8a4e-47e2-9316-5b6028662c02",
"metadata": {},
"outputs": [],
"source": [
"DATASET_NAME = \"merlin\"\n",
"dataset_path = Path.home() / DATASET_NAME\n",
"if not Path(dataset_path).exists():\n",
" repo_url = create_repo(name=DATASET_NAME, repo_type=\"dataset\")\n",
" repo = Repository(local_dir=str(dataset_path), clone_from=repo_url)\n",
" repo.lfs_track(\"*.jsonl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "005d059f-5de4-4f14-bde3-0cc9ff2435c0",
"metadata": {},
"outputs": [],
"source": [
"MERLN_EXMARALDA_BASE = Path.home() / Path(\n",
" \"Downloads/MERLIN Written Learner Corpus for Czech, German, Italian 1.1/merlin-exmaralda-v1.1/\"\n",
")\n",
"\n",
"for lang in (\"german\", \"czech\", \"italian\"):\n",
" lang_docs = []\n",
" for path in (MERLN_EXMARALDA_BASE / lang).glob(\"*.exb\"):\n",
" with open(path) as fp:\n",
" xml = HTMLParser(fp.read())\n",
" docs = list(process_doc(xml, path))\n",
" lang_docs.extend(docs)\n",
" Dataset.from_dict(pd.DataFrame(lang_docs)).to_json(dataset_path / f\"{lang}.jsonl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9d396b2-98dc-4c04-950f-0332a3a6d751",
"metadata": {},
"outputs": [],
"source": [
"repo.push_to_hub()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91377eaf-ffac-4df0-9c85-4f6ee979f99f",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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