{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eba2ee81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09e8150022c94f569f19b76663ffb89f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/7.79k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No config specified, defaulting to: kp_times/raw\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset kp_times/raw to /Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___kp_times/raw/1.1.0/81f75cd972e595c55ef8cc865e898b0bc01ce7d220287a246b566b7417f07274...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f40668afdd0428eb9bec18770b4bf3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f17b141c71d4f03ac58df7b4d1133cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset kp_times downloaded and prepared to /Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___kp_times/raw/1.1.0/81f75cd972e595c55ef8cc865e898b0bc01ce7d220287a246b566b7417f07274. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
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       "model_id": "716568658d6749da8a0926dcb1fb384e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset('taln-ls2n/kptimes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ba72244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb61d182cdc14ea7868eb258413ca117",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/259923 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abf721e69b3f46079166a1986514b5bf",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "827aab4cd5414084adf841e52d4899c4",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# keyphrases: 5.03\n",
      "% P: 46.64\n",
      "% R: 15.11\n",
      "% M: 28.89\n",
      "% U: 9.36\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "P, R, M, U, nb_kps = [], [], [], [], []\n",
    "\n",
    "for split in ['train', 'validation', 'test']:\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(\"# 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": 4,
   "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": null,
   "id": "047ab1cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45f4357088854088870320517821adc4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/259923 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "doc_len = []\n",
    "for split in ['train', 'validation', 'test']:\n",
    "    for sample in tqdm(dataset[split]):\n",
    "        doc_len.append(len(nlp(sample[\"title\"])) + len(nlp(sample[\"abstract\"])))\n",
    "print(\"avg doc len: {:.1f}\".format(sum(doc_len)/len(doc_len)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d55f0f0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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