add notebook that was used to load the data

load_dataset_merlin.ipynb

@@ -0,0 +1,142 @@
+{
+ "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
+}