{
 "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"
     ]
    },
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       "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)))        "
   ]
  }
 ],
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