{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eba2ee81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No config specified, defaulting to: wikinews/raw\n",
      "Reusing dataset wikinews (/Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___wikinews/raw/1.0.0/aa15bd435a75a532fac6070fe8169812db6efd9d00c6fbac93992165536d8183)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51588bf1a2714239b22d99eeac8f0db7",
       "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/termith-eval')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ba72244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc2eac8de82a4851901c76d873c7546f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# keyphrases: 11.81\n",
      "% P: 40.60\n",
      "% R: 7.32\n",
      "% M: 19.28\n",
      "% U: 32.80\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(\"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": "7d2dc99496ef4579b3b027ca651ed359",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg doc len: 156.9\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
}