File size: 5,326 Bytes
34c0079 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "eba2ee81",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No config specified, defaulting to: inspec/raw\n",
"Reusing dataset inspec (/Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___inspec/raw/1.0.0/0980ea60c840383eb282b6272baba681a578ed092f61438b008254c70d20f32b)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ad1b39fd3294bcfabe57a9acf24986e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset('taln-ls2n/wikinews-fr-100')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4ba72244",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bded16e4b0a43ad8907144bce073d0c",
"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": [
"statistics for test\n",
"# keyphrases: 9.64\n",
"% P: 95.91\n",
"% R: 1.40\n",
"% M: 0.85\n",
"% U: 1.84\n"
]
}
],
"source": [
"from tqdm.notebook import tqdm\n",
"\n",
"for split in ['test']:\n",
" \n",
" P, R, M, U, nb_kps = [], [], [], [], []\n",
" \n",
" for sample in tqdm(dataset[split]):\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(\"statistics for {}\".format(split))\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(\"fr_core_news_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": "135b8cd19d054319a445df200d82cc65",
"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": [
"statistics for test\n",
"avg doc len: 306.9\n"
]
}
],
"source": [
"for split in ['test']:\n",
" doc_len = []\n",
" for sample in tqdm(dataset[split]):\n",
" doc_len.append(len(nlp(sample[\"title\"])) + len(nlp(sample[\"abstract\"])))\n",
" \n",
" print(\"statistics for {}\".format(split))\n",
" print(\"avg doc len: {:.1f}\".format(sum(doc_len)/len(doc_len)))\n",
" "
]
}
],
"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": "3.10.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|