Datasets:
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "eba2ee81",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No config specified, defaulting to: sem_eval/raw\n",
"Reusing dataset sem_eval (/Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___sem_eval/raw/1.0.0/b40e008b5c96137733e24d9d244d70aa1fe6353ee65e180d8f6948af4027fbe4)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9379b6f5f5d1483ab184db7486ac67b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset('taln-ls2n/semeval-2010-pre')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4ba72244",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c14c3725089d4b5284e36df4cf90d3da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/100 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"# keyphrases: 14.66\n",
"% P: 40.11\n",
"% R: 8.34\n",
"% M: 27.12\n",
"% U: 24.43\n"
]
}
],
"source": [
"from tqdm.notebook import tqdm\n",
"\n",
"P, R, M, U, nb_kps = [], [], [], [], []\n",
" \n",
"for sample in tqdm(dataset['test']):\n",
" nb_kps.append(len(sample[\"keyphrases\"]))\n",
" P.append(sample[\"prmu\"].count(\"P\") / nb_kps[-1])\n",
" R.append(sample[\"prmu\"].count(\"R\") / nb_kps[-1])\n",
" M.append(sample[\"prmu\"].count(\"M\") / nb_kps[-1])\n",
" U.append(sample[\"prmu\"].count(\"U\") / nb_kps[-1])\n",
" \n",
"print(\"# keyphrases: {:.2f}\".format(sum(nb_kps)/len(nb_kps)))\n",
"print(\"% P: {:.2f}\".format(sum(P)/len(P)*100))\n",
"print(\"% R: {:.2f}\".format(sum(R)/len(R)*100))\n",
"print(\"% M: {:.2f}\".format(sum(M)/len(M)*100))\n",
"print(\"% U: {:.2f}\".format(sum(U)/len(U)*100))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "52dda817",
"metadata": {},
"outputs": [],
"source": [
"import spacy\n",
"\n",
"nlp = spacy.load(\"en_core_web_sm\")\n",
"\n",
"# https://spacy.io/usage/linguistic-features#native-tokenizer-additions\n",
"\n",
"from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER\n",
"from spacy.lang.char_classes import CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS\n",
"from spacy.util import compile_infix_regex\n",
"\n",
"# Modify tokenizer infix patterns\n",
"infixes = (\n",
" LIST_ELLIPSES\n",
" + LIST_ICONS\n",
" + [\n",
" r\"(?<=[0-9])[+\\-\\*^](?=[0-9-])\",\n",
" r\"(?<=[{al}{q}])\\.(?=[{au}{q}])\".format(\n",
" al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES\n",
" ),\n",
" r\"(?<=[{a}]),(?=[{a}])\".format(a=ALPHA),\n",
" # ✅ Commented out regex that splits on hyphens between letters:\n",
" # r\"(?<=[{a}])(?:{h})(?=[{a}])\".format(a=ALPHA, h=HYPHENS),\n",
" r\"(?<=[{a}0-9])[:<>=/](?=[{a}])\".format(a=ALPHA),\n",
" ]\n",
")\n",
"\n",
"infix_re = compile_infix_regex(infixes)\n",
"nlp.tokenizer.infix_finditer = infix_re.finditer"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "047ab1cc",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "209e7faf7c454aeabc936c07919ac1fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"avg doc len: 203.1\n"
]
}
],
"source": [
"doc_len = []\n",
"for sample in tqdm(dataset['test']):\n",
" doc_len.append(len(nlp(sample[\"title\"])) + len(nlp(sample[\"abstract\"])))\n",
" \n",
"print(\"avg doc len: {:.1f}\".format(sum(doc_len)/len(doc_len))) "
]
}
],
"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": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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