{
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m         tokens\u001b[38;5;241m.\u001b[39mappend(splits[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     17\u001b[0m         pos_tags\u001b[38;5;241m.\u001b[39mappend(splits[\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m---> 18\u001b[0m         ner_tags\u001b[38;5;241m.\u001b[39mappend(\u001b[43msplits\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mrstrip())\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# last example\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tokens:\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "with open(\"data/train.conll\", encoding=\"utf-8\") as f:\n",
    "    guid = 0\n",
    "    tokens = []\n",
    "    pos_tags = []\n",
    "    ner_tags = []\n",
    "    for line in f:\n",
    "        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\n",
    "            if tokens:\n",
    "                guid += 1\n",
    "                tokens = []\n",
    "                pos_tags = []\n",
    "                ner_tags = []\n",
    "        else:\n",
    "            print(guid)\n",
    "            splits = line.split(\" \")\n",
    "            tokens.append(splits[0])\n",
    "            pos_tags.append(splits[1])\n",
    "            ner_tags.append(splits[2].rstrip())\n",
    "\n",
    "    # last example\n",
    "    if tokens:\n",
    "        print(\"lst\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset plod-cw (/home/diptesh/.cache/huggingface/datasets/surrey-nlp___plod-cw/PLOD-CW/0.0.5/ded93459451683583207c3ccb6a22ebeeafd54733e72757b6f73806d9aca6e83)\n"
     ]
    },
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       "initial": 0,
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       "ncols": null,
       "nrows": null,
       "postfix": null,
       "prefix": "",
       "rate": null,
       "total": 3,
       "unit": "it",
       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f468deeb0f34c0b8fe8bdd94301ba38",
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      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"surrey-nlp/PLOD-CW\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1072\n",
      "126\n",
      "153\n"
     ]
    }
   ],
   "source": [
    "print(len(dataset['train']))\n",
    "print(len(dataset['validation']))\n",
    "print(len(dataset['test']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "print(len(dataset['train'][0]['tokens']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "323\n"
     ]
    }
   ],
   "source": [
    "split='train'\n",
    "maxLen = 0\n",
    "for i in range(len(dataset[split])):\n",
    "    instanceLen = len(dataset['train'][i]['tokens'])\n",
    "    if instanceLen > maxLen:\n",
    "        maxLen = instanceLen\n",
    "\n",
    "print(maxLen)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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