NER_playground.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fcbba297-cfb1-4518-aa12-895fedbb4854",
   "metadata": {},
   "source": [
    "## Loading Libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "38b37bf4-fcf0-42a4-aace-0153642da772",
   "metadata": {},
   "outputs": [],
   "source": [
    "import transformers\n",
    "\n",
    "from transformers import BertForTokenClassification, BertTokenizer\n",
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae19fad-067d-43ed-a777-7c3115f1afd0",
   "metadata": {},
   "source": [
    "## Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5caec44-3ed1-4e22-8f10-c18a3f784cf3",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Reusing dataset lener_br (/home/caiotulio/.cache/huggingface/datasets/lener_br/lener_br/1.0.0/4a8c97e6813b5c2d85a50faf0a3e6c24ea82f4a9044e6e9e8b24997d27399382)\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"lener_br\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "470fa702-6131-4069-9a4b-9de232aea973",
   "metadata": {},
   "outputs": [],
   "source": [
    "NER_tags = [\"O\", \"B-ORGANIZACAO\", \"I-ORGANIZACAO\", \"B-PESSOA\", \"I-PESSOA\", \"B-TEMPO\", \"I-TEMPO\", \"B-LOCAL\", \"I-LOCAL\", \"B-LEGISLACAO\", \"I-LEGISLACAO\", \"B-JURISPRUDENCIA\", \"I-JURISPRUDENCIA\"]\n",
    "num_labels = len(NER_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06622ce3-6839-4b7b-bbff-241e36694fda",
   "metadata": {},
   "source": [
    "## Loading model checkpoint for tokenizer and for model weight initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0ea0cc9-1004-47e6-91fd-2a248a417afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "PRE_TRAINED_MODEL = 'neuralmind/bert-base-portuguese-cased'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3ef4266-7570-4bec-a3a7-8166a8291231",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49b42589-7fb9-4b3c-9a5a-d16c73af34e7",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertForTokenClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight']\n",
      "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForTokenClassification were not initialized from the model checkpoint at neuralmind/bert-base-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = BertForTokenClassification.from_pretrained(\n",
    "    PRE_TRAINED_MODEL,\n",
    "    num_labels=num_labels,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f403a0f-4de0-4b30-99c7-bf40bf0bb9ff",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "model.num_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d62d089-c544-4e80-9614-259be1093612",
   "metadata": {},
   "source": [
    "# Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1fb1ae1-d8b7-47b7-a382-d9435298ca26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import rcParams\n",
    "\n",
    "from matplotlib import rc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1b6c42d9-bca6-458a-bd3c-d87266ed6ede",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f5b6844c730>"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "\n",
    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
    "\n",
    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
    "\n",
    "rcParams['figure.figsize'] = 12, 8\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "\n",
    "np.random.seed(RANDOM_SEED)\n",
    "\n",
    "torch.manual_seed(RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b870c6ab-fb9f-4e03-af5d-0cd1af5776e6",
   "metadata": {},
   "source": [
    "## Analysing number of labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34ba8a33-798d-41bb-b0d4-be48cd62b11e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = np.concatenate(dataset['train'][:]['ner_tags'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ebbafb24-af1d-47b2-bc32-3cdcc6047eea",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5aa2ecb820>"
      ]
     },
     "metadata": {},
     "execution_count": 14
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "sns.countplot(tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79a9df39-f798-45e9-9867-11c34b33c12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "identified_tags = list(filter(lambda t: t > 0, tags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7af57bcf-877b-40bd-ad9b-46dd496b7e8d",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5aa2e57670>"
      ]
     },
     "metadata": {},
     "execution_count": 16
    },
    {
     "output_type": "display_data",
     "data": {
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E//znP5fH46k3/u9//1s///nPW7QoAAAAAI0LOMQ3tAsvST/88IMiIiJarCAAAAAATQs704KioiJJks1m0//8z/+offv2vrna2lr985//VPfu3VuvQgAAAAB+zhji165dK+nUTvxbb72l0NBQ31x4eLhiYmL029/+tvUqBAAAAODnjCF+w4YNkqTMzEwVFRWpU6dOrV4UAAAAgMadMcTXWbp0aWvWAQAAACBAAYd4Sfrwww/1/vvv67vvvlNtba3f3JNPPtmihQEAAABoWMBPp1m0aJHuvfdebdiwQYcOHdLhw4f9PlrKrl27NH78eCUlJSk1NVVTp071zZWWlmrkyJFKTEzU4MGDtWnTJr9rKysrlZ2drZSUFKWlpWnOnDmqqanxW7Ny5UoNGjRIDodDmZmZ2r9/f4vVDgAAAJwLAe/Ev/rqq8rJyVFmZmarFbN3716NHz9e9913n37/+98rJCREe/fulSSVl5dr4sSJuvPOO/X0009r48aNmjJlioqLi9WtWzdJUl5ennbu3KnFixersrJSjzzyiCIjIzVlyhRJ0pYtW5Sbm6vc3FwlJSVp3rx5mjRpktavX6+wsGb9pQQAAAAQNAHvxB89elQ33XRTK5YiFRYW6rbbbtOUKVN0zTXX6Oqrr9att94qSSouLlZkZKRycnLUvXt3TZ48WQ6HQytWrJAkVVRUqLi4WLNmzZLD4VBGRoamTZum1157zXf0Z9myZRoyZIhGjRql+Ph4FRQU6PDhw9q8eXOr9gUAAAC0pIBD/M0336ySkpJWK6SmpkabN2/WFVdcoczMTPXr10/333+/ysrKJEkul0tpaWmy2Wy+azIyMlRaWirp1DEcm82m1NRUv3m3262DBw/67pGenu6bb9++vRwOh+8eAAAAgAkCPkOSnJysZ599Vnv27NG1116r8PBwv/lhw4b9pEI8Ho+qqqq0aNEizZgxQ4mJiXr11Vc1YcIEvfXWW/J4POrZs6ffNVFRUXK73ZIkt9utTp06+T3HPjo62jcXFxcnj8fjGzt9jcfjaXa9O3fubPY1AADADH379g12CWdlx44dwS4B50jAIT4vL0+S9Morr9Sbs9lsPznE1x15uf322zVmzBjf1xwwYIDeeecdWZbV5PUNzZ++a9/SEhIS1K5du1a7PwAAQHOZ+ssH6jtx4kSTm8YBh/h//etfLVJQY6KiohQaGqorr7zSNxYeHq7Y2Fh9/fXXstvt9XbMy8vLZbfbJUldunRRRUWFampqfLvxdbv0dWsa2nX3eDx+XxMAAABo6wI+E9/aIiIi1LNnT79HPnq9Xh06dEhXXHGFHA6Htm7d6ndNSUmJkpKSJEm9evWSZVnavn2737zdbldMTIwk1btHVVWVXC6X7x4AAACACQLeiS8qKmpyvu4xjj/Ffffdp5ycHKWlpSkxMVFLly5VSEiIbrrpJlVXV6uoqEizZ8/WmDFjtHHjRpWWlio/P1+S1LlzZw0dOlT5+fmaPXu2vv/+exUWFmrs2LEKCTn1u8q4ceM0ceJEXXfddb5HTHbt2lX9+/f/ybUDAAAA50rAIX7t2rV+n3u9Xn3zzTeKiIjQZZdd1iIhftiwYXK73Zo7d66OHj0qh8Ohl19+WR06dFCHDh20cOFC5eXl6fXXX1dsbKyKiop8z4iXpNzcXOXl5Wn8+PEKDw/XiBEjlJWV5ZvPyMjQ448/rnnz5unIkSNKTk7WggUL6r1JFwAAAGjLbNaZ3jHaBLfbrRkzZmjMmDG6+eabW7KuNqvuTQa8sRUAgPPb31Z8F+wSmuWO0V2CXQJa0Jky5086E2+32zVt2jT98Y9//Cm3AQAAANAMP/mNrWFhYfr2229bohYAAAAAAQj4TPw///lPv88ty9K3336rRYsWKSEhocULAwAAANCwgEP82LFjZbPZ6v2jSikpKb4nxAAAAABofQGH+I0bN/p9HhISoujoaN7cCQAAAJxjAYf4n/3sZ61ZBwAAAIAABRziJemLL77QokWL9Nlnn8lms6l79+761a9+pSuvvLK16gMAAADwIwE/neb999/XsGHD9OmnnyopKUmJiYnavXu37rzzTm3ZsqU1awQAAABwmoB34p955hn98pe/VE5Ojt94fn6+5s6dK6fT2eLFAQAAAKgv4J34PXv26Je//GW98bFjx6qsrKxFiwIAAADQuIBDfIcOHXT48OF641999ZUiIyNbtCgAAAAAjQs4xN9yyy2aNWuWNm/erKqqKlVVVendd99Vbm6ubrnlltasEQAAAMBpAj4TP2PGDD366KOaOHGibDabb/y2227TI4880irFAQAAAKgv4BDfoUMHPffcc/ryyy/12WefSZKuueYaxcbGtlpxAAAAAOoLOMRPnTpVPXv21H/9138pLi7ON75w4ULt3r1bhYWFrVIgAAAAAH8Bn4nftm2bbrzxxnrjAwYM0Pbt21u0KAAAAACNCzjEHzt2TO3bt683ftFFF6mioqJFiwIAAADQuIBDfFxcnN5///164++//75iYmJatCgAAAAAjQv4TPzYsWP1pz/9SdXV1brhhhtks9n03nvv6fnnn9f06dNbs0YAAAAApwk4xI8bN05ut1t//vOf9dRTT0mSIiIiNGHCBGVmZrZagQAAAAD8BRziJenhhx/WxIkTtWfPHklS9+7dGzwnDwAAAKD1NCvES9LFF18sh8PRGrUAAAAACEDAb2wFAAAA0DYQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIREMtbHewSms3EmgEAAAIRFuwCYAZbWIS+fO7uYJfRLHEPO4NdAgAAQKtgJx4AAAAwDCEeAAAAMAwhHgAAADAMIR4AAAAwDCEeAAAAMEybDfEPPvigevTooa1bt/rGSktLNXLkSCUmJmrw4MHatGmT3zWVlZXKzs5WSkqK0tLSNGfOHNXU1PitWblypQYNGiSHw6HMzEzt37//nPQDAAAAtJQ2GeLXrFmjqqoqv7Hy8nJNnDhRKSkpWr16tYYPH64pU6Zo3759vjV5eXn65JNPtHjxYhUWFmrdunWaP3++b37Lli3Kzc1VVlaWnE6n7Ha7Jk2aJK/Xe65aAwAAAH6yNhfiv/nmGz377LPKz8/3Gy8uLlZkZKRycnLUvXt3TZ48WQ6HQytWrJAkVVRUqLi4WLNmzZLD4VBGRoamTZum1157TbW1tZKkZcuWaciQIRo1apTi4+NVUFCgw4cPa/Pmzee8TwAAAOBstbl/7CknJ0eTJ0/WFVdc4TfucrmUlpYmm83mG8vIyNAHH3wgSdq1a5dsNptSU1P95t1utw4ePKi4uDi5XC5Nnz7dN9++fXs5HA6VlpZq4MCBzapz586dZ9Oesfr27RvsEs7Kjh07gl0CAMBAvO6hrWtTIX758uXyer0aM2ZMvTmPx6OePXv6jUVFRcntdkuS3G63OnXqpNDQUN98dHS0by4uLk4ej8c3dvoaj8fT7FoTEhLUrl27Zl+Hc8vUH8IAAJwNXvfOHydOnGhy07jNhPivvvpKRUVFWr58eYPzlmU1eX1D86fv2gMAAADnizYT4nfv3q3vvvtOt956q9/4fffdpxEjRshut9fbMS8vL5fdbpckdenSRRUVFaqpqfHtxtft0tetaWjX3ePx6Morr2yVngAAAIDW0Gbe2Jqenq61a9dqzZo1vg9Jys/P19SpU+VwOPweNylJJSUlSkpKkiT16tVLlmVp+/btfvN2u10xMTGSVO8eVVVVcrlcvnsAAAAAJmgzIT4yMlLx8fF+H5IUExOjyy+/XMOGDdPx48c1e/Zs7d27VwsXLlRpaalGjx4tSercubOGDh2q/Px8uVwulZSUqLCwUGPHjlVIyKk2x40bp3Xr1snpdGrPnj2aOXOmunbtqv79+wetbwAAAKC52sxxmjOJiorSwoULlZeXp9dff12xsbEqKipSt27dfGtyc3OVl5en8ePHKzw8XCNGjFBWVpZvPiMjQ48//rjmzZunI0eOKDk5WQsWLFB4eHgQOgIAAADOjs060ztG4afuncIX4tNpvnzu7mCX0CxxDzuDXQIAwGB/W/FdsEtoljtGdwl2CWhBZ8qcbeY4DQAAAIDAEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDiAQAAAMMQ4gEAAADDEOIBAAAAwxDigQuAt6Y62CU0i2n1AgBwroUFuwAArS8sNEILlt4W7DICNjnzrWCXAABAm8ZOPAAAAGAYQjwAAABgGEI8AAAAYBhCPAAAAGAYQjwAAABgGEI8AAAAYBhCPAAAAGAYQjwAAADOK5a3NtglNFtza+YfewIAAMB5xRYWom+e3RLsMprl8qkZzVrPTjwAAABgGEI8AAAAYBhCPAAAAGAYQjwAAABgGEI8AAAAYBhCPAAAAGAYQjwAAABgGEI8AAAAYBhCPAAAAGAYQjwAAABgGEI8AAAAYBhCPAAAAGAYQjwAAABgGEI8AAAAYBhCPAAAAGAYQnwLsbw1wS6h2UysGQAAAFJYsAs4X9jCQnVk/qvBLqNZLs26J9glAAAA4CywEw8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     },
     "metadata": {}
    }
   ],
   "source": [
    "sns.countplot(identified_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164fbb0e-1e94-4ea8-b0ba-e3199f08312f",
   "metadata": {},
   "source": [
    "## Analysing sentence size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82c318ee-5b5b-44e7-bee4-72cb5f352f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_length(tokens):\n",
    "    return [len(s) for s in tokens]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retokenize(tokens):\n",
    "    retokenized = []\n",
    "    for token in tokens:\n",
    "        s = \" \".join(token)\n",
    "        tok = tokenizer.tokenize(s)\n",
    "        retokenized.append(tok)\n",
    "    return retokenized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a7866183-550f-43a6-8081-f20e6949ca5e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokens = dataset['train'][:]['tokens']\n",
    "tokens = retokenize(tokens)\n",
    "lengths = get_length(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f8b1f23-e9f7-4db1-a342-8f3ae1a1d81a",
   "metadata": {},
   "outputs": [],
   "source": [
    "teste = list(filter(lambda l: l > 200,lengths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "958a378f-5522-4c8d-acd7-b3b66c358e2a",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5aa020dc40>"
      ]
     },
     "metadata": {},
     "execution_count": 22
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "sns.histplot(lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "7828"
      ]
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    223.0\n",
       "Name: 0.99, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "df.quantile(0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1350153/2624658217.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m0.01\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "0.01*len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a9a315bb0>"
      ]
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "sns.boxplot(lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b232a53e-0849-477a-9e70-4777cfc4df5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "longer_sentences = list(filter(lambda t: len(t) > 512, tokens)) # Bert maximum sentence size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb9f5bd1-99ed-48ff-8a4e-0a5e73402187",
   "metadata": {},
   "outputs": [],
   "source": [
    "\" \".join(longer_sentences[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7b8fcf3-83f5-4b65-9dfc-d9c201f21952",
   "metadata": {},
   "outputs": [],
   "source": [
    "TOKEN_SIZE_THRESHOLD = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c27f7b57-a4f6-4dc8-abc1-3740d9f71f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize(token):\n",
    "    return 0 if len(token) < TOKEN_SIZE_THRESHOLD else 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbca84ff-9568-4597-b405-e1774cff0443",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_by_size = list(map(categorize, tokens))\n",
    "larger_than_threshold = np.count_nonzero(tokens_by_size)\n",
    "smaller_than_threshold = len(tokens_by_size) - larger_than_threshold\n",
    "\n",
    "print(f\"Tokens menores que {TOKEN_SIZE_THRESHOLD}: {smaller_than_threshold}; Maiores que {TOKEN_SIZE_THRESHOLD}: {larger_than_threshold}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f938cd6-bddf-4d65-81eb-8c45d4680e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"A maior sentença tem comprimento {np.max(lengths)}\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a82f2e38-ff29-496b-930d-3ed9a6e553be",
   "metadata": {},
   "source": [
    "A esmagadora maioria das sentenças tem tamanho menor do que 256, acho que dá só pra descartar essas maiores. (analise meio porca, mas enfim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90b4249-4153-4c1b-adb8-abe917469e02",
   "metadata": {},
   "source": [
    "## Testing out the tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77460e98-a055-4440-89e2-b73c7f66fe5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = dataset['test'][10] # numero aleatório\n",
    "sentence = sample['tokens']\n",
    "tags = sample['ner_tags']\n",
    "print(sentence)\n",
    "print(tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a09c6c9d-0317-4db8-90b6-ac6e086efe80",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.tokenize(\" \".join(sentence))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd5e7773-ac1b-4d73-b659-d324b4c14ed3",
   "metadata": {},
   "source": [
    "(O output do tokenizer do bertimbau é diferente da tokenização fornecida pelo dataset. O dataset aparentemente fez um tokenização baseado em espaços. Um problema de tokenizar do zero seria lidar com pontuação estranha de texto jurídico)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbc6bbc-656c-4b90-bcd6-aba29257f77f",
   "metadata": {},
   "source": [
    "# Pipeline to pass a single sentence to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88facd52-bf9c-4dcf-8bcd-7bb3e8d26883",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_input = sample['tokens']\n",
    "tokenized_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51399842-f811-48ab-9fe0-003a5ba2ef35",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = tokenizer.convert_tokens_to_ids(tokenized_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac4e1f9-86b5-4a88-9f81-05fa9ab210f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeb9fa32-7edd-48e3-a359-a4829467c33c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.decode(ids) # Varios unknows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a044551c-7436-4738-81d9-da23c356bf06",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_tensor = torch.tensor([ids])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edbd313f-0126-43a7-a835-9f2f6c2a9f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = model(input_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1981abab-29a9-4095-9dd8-744bddb96f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "softmax = torch.nn.Softmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8809d4c5-c9b9-499e-b0a8-a222f8f9645c",
   "metadata": {},
   "outputs": [],
   "source": [
    "output.logits[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b880f2cb-d231-4dfb-9bc4-2959e55e0432",
   "metadata": {},
   "source": [
    "## Comparing retokenization with just passing the sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "638e08b8-013f-44a6-a62d-d1fb5227f293",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01bbac75-ebfa-4dc2-902b-a4613a05efd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.decode(tokenizer.convert_tokens_to_ids(tokenized_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6160881-0a57-486d-87d5-0d98d558ae76",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.decode(\n",
    "        tokenizer.convert_tokens_to_ids(\n",
    "            tokenizer.tokenize(\n",
    "                \" \".join(tokenized_input)\n",
    "            )\n",
    "        )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e83c7f9-c10b-42c8-9a9b-a18e14647300",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_decode(tokens):\n",
    "    ids = tokenizer.convert_tokens_to_ids(tokens)\n",
    "    return tokenizer.decode(ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "277cb70d-31c1-4ef4-abcb-256aae2df778",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vanilla_approach(tokens):\n",
    "    sentence = \" \".join(tokens)\n",
    "    return encode_decode(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d620581a-2dfa-4d9b-92b7-d9f6156e2032",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lazy_approach(tokens):\n",
    "    return encode_decode(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e558be-1553-49e4-8af4-e0eab14c8a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c31af49-4d63-41c8-a3b9-758dcbdbf362",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_unknowns(data):\n",
    "    \"\"\"\n",
    "    Compares the ammount of unknow tokens from two different tokenizations approaches: the vanilla one (retokenizing the data\n",
    "    in the dataset) and the lazy one (just using the pretokenized data)\n",
    "    \"\"\"\n",
    "    freq = defaultdict()\n",
    "    unk_vanilla = 0\n",
    "    unk_lazy = 0\n",
    "    total_tokens = 0\n",
    "    for i in range(len(data)):\n",
    "        sample = data[i]['tokens']\n",
    "        total_tokens += len(sample)\n",
    "        lazy_decoded = lazy_approach(sample)\n",
    "        vanilla_decoded = vanilla_approach(sample)\n",
    "        unk_lazy += lazy_decoded.count('[UNK]')\n",
    "        unk_vanilla += vanilla_decoded.count('[UNK]')\n",
    "    return unk_vanilla, unk_lazy, total_tokens\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a778fad0-a73e-4665-9757-e1a924d5f09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_unknowns(dataset['train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "908bf18b-bf2e-4bf5-996c-77415eb0ace0",
   "metadata": {},
   "outputs": [],
   "source": [
    "100 * 54796/229277"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b6d80f-fe05-40be-9b2e-5c041c0ea7cf",
   "metadata": {},
   "source": [
    "23% dos tokens são perdidos no processo lazy :O"
   ]
  }
 ],
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