Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
ArXiv:
License:
File size: 5,026 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
},
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m tokens\u001b[38;5;241m.\u001b[39mappend(splits[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 17\u001b[0m pos_tags\u001b[38;5;241m.\u001b[39mappend(splits[\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m---> 18\u001b[0m ner_tags\u001b[38;5;241m.\u001b[39mappend(\u001b[43msplits\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mrstrip())\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# last example\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tokens:\n",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"with open(\"data/train.conll\", encoding=\"utf-8\") as f:\n",
" guid = 0\n",
" tokens = []\n",
" pos_tags = []\n",
" ner_tags = []\n",
" for line in f:\n",
" if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\n",
" if tokens:\n",
" guid += 1\n",
" tokens = []\n",
" pos_tags = []\n",
" ner_tags = []\n",
" else:\n",
" print(guid)\n",
" splits = line.split(\" \")\n",
" tokens.append(splits[0])\n",
" pos_tags.append(splits[1])\n",
" ner_tags.append(splits[2].rstrip())\n",
"\n",
" # last example\n",
" if tokens:\n",
" print(\"lst\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reusing dataset plod-cw (/home/diptesh/.cache/huggingface/datasets/surrey-nlp___plod-cw/PLOD-CW/0.0.5/ded93459451683583207c3ccb6a22ebeeafd54733e72757b6f73806d9aca6e83)\n"
]
},
{
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"postfix": null,
"prefix": "",
"rate": null,
"total": 3,
"unit": "it",
"unit_divisor": 1000,
"unit_scale": false
},
"application/vnd.jupyter.widget-view+json": {
"model_id": "1f468deeb0f34c0b8fe8bdd94301ba38",
"version_major": 2,
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"text/plain": [
" 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datasets import load_dataset\n",
"dataset = load_dataset(\"surrey-nlp/PLOD-CW\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1072\n",
"126\n",
"153\n"
]
}
],
"source": [
"print(len(dataset['train']))\n",
"print(len(dataset['validation']))\n",
"print(len(dataset['test']))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15\n"
]
}
],
"source": [
"print(len(dataset['train'][0]['tokens']))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"323\n"
]
}
],
"source": [
"split='train'\n",
"maxLen = 0\n",
"for i in range(len(dataset[split])):\n",
" instanceLen = len(dataset['train'][i]['tokens'])\n",
" if instanceLen > maxLen:\n",
" maxLen = instanceLen\n",
"\n",
"print(maxLen)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "hfdataset",
"language": "python",
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},
"language_info": {
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
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