Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
ArXiv:
File size: 2,537 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "6aa5fb66",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "445679f5",
"metadata": {},
"outputs": [],
"source": [
"def convert_txt_to_jsonl(base_url, domain):\n",
" \n",
" urls = {\n",
" \"train\": f\"{base_url}/{domain}/train.txt\",\n",
" \"validation\": f\"{base_url}/{domain}/dev.txt\",\n",
" \"test\": f\"{base_url}/{domain}/test.txt\",\n",
" }\n",
"\n",
" os.makedirs(domain, exist_ok=True)\n",
"\n",
" for split, url in urls.items():\n",
" text = requests.get(url).text.strip().splitlines()\n",
" samples, tokens, tags = [], [], []\n",
" guid = 0\n",
"\n",
" for line in text:\n",
" if not line.strip():\n",
" if tokens:\n",
" samples.append({\"id\": str(guid), \"tokens\": tokens, \"ner_tags\": tags})\n",
" guid += 1\n",
" tokens, tags = [], []\n",
" else:\n",
" token, tag = line.split(\"\\t\")\n",
" tokens.append(token)\n",
" tags.append(tag)\n",
" \n",
" if tokens:\n",
" samples.append({\"id\": str(guid), \"tokens\": tokens, \"ner_tags\": tags})\n",
"\n",
" with open(f\"{domain}/{split}.json\", \"w\", encoding=\"utf-8\") as f:\n",
" for s in samples:\n",
" f.write(f\"{s}\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "739fc387",
"metadata": {},
"outputs": [],
"source": [
"domains = [\"conll2003\", \"politics\", \"science\", \"music\", \"literature\", \"ai\"]\n",
"base_url = \"https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data\"\n",
"\n",
"for domain in domains:\n",
" convert_txt_to_jsonl(domain)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|