{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eba2ee81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No config specified, defaulting to: taln_archives/raw\n",
      "Reusing dataset taln_archives (/Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___taln_archives/raw/1.0.0/edf5310cc76b9c9758d5785dec61311aadfc728038d62dcb784086552dce4173)\n"
     ]
    },
    {
     "data": {
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       "model_id": "79acb44f1fca4c428c183d6762f7bb94",
       "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/taln-archives')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ba72244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6b22792bf51480bac2b203ef7549c3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "statistics for test\n",
      "# keyphrases: 4.12\n",
      "% P: 53.83\n",
      "% R: 12.32\n",
      "% M: 21.69\n",
      "% U: 12.16\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": "806cc5748b4a40b490a138d38a3590fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "statistics for test\n",
      "avg doc len: 138.3\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)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03a61ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "035722c1a78f42e6be7bd8333df9b124",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fully_translated: 456 / 0.38\n",
      "partially_translated: 719 / 0.60\n"
     ]
    }
   ],
   "source": [
    "fully_translated = 0\n",
    "partially_translated = 0\n",
    "for sample in tqdm(dataset['test']):\n",
    "    title, abstract, keywords = sample[\"translation\"]\n",
    "    if len(title) and len(abstract) and len(keywords):\n",
    "        fully_translated += 1\n",
    "    elif len(title) or len(abstract) or len(keywords):\n",
    "        partially_translated += 1\n",
    "print(\"fully_translated: {} / {:.2f}\".format(fully_translated, fully_translated/len(dataset['test'])))\n",
    "print(\"partially_translated: {} / {:.2f}\".format(partially_translated, partially_translated/len(dataset['test'])))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f8dec4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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