{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "90e02bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "import transformers\n",
    "import torch\n",
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "285335e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-b7be9a9d71194d1a\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /Users/fixed/.cache/huggingface/datasets/csv/default-b7be9a9d71194d1a/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88b1a7629b6345709dbd1adb4486ec58",
       "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": "fcf84fc7b8af491b8ee7217d258bc754",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /Users/fixed/.cache/huggingface/datasets/csv/default-b7be9a9d71194d1a/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c47a15fa466f4b71ada94b7c317be40a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "dataset_train_test = load_dataset('csv', \n",
    "                                  data_files={'train': 'train_data.csv', 'test': 'test_data.csv', 'val': 'val_data.csv'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a097810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 44928\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 11981\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 14976\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9d9f837b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77c3bc469e924d90b410c9a053b3d3fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/45 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c94567c41c54910bcbd02f33078f057",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "642f18f8d18e4176b114420edfa959b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n",
    "\n",
    "\n",
    "tokenized_datasets = dataset_train_test.map(tokenize_function, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73d1be33",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "{'text': 'strategic behaviour and indicative price diffusion in paris stock   exchange auctions.   we report statistical regularities of the opening and closing auctions of french equities, focusing on the diffusive properties of the indicative auction price. two mechanisms are at play as the auction end time nears: the typical price change magnitude decreases, favoring underdiffusion, while the rate of these events increases, potentially leading to overdiffusion. a third mechanism, caused by the strategic behavior of traders, is needed to produce nearly diffusive prices: waiting to submit buy orders until sell orders have decreased the indicative price and vice-versa. ',\n",
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      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_datasets['test'][6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "83fc07e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def torch_dataset(dataset):\n",
    "    dataset = dataset.remove_columns([\"text\"])\n",
    "    dataset = dataset.rename_column(\"label\", \"labels\")\n",
    "    dataset.set_format(\"torch\")\n",
    "    return dataset\n",
    "\n",
    "train_dataset = torch_dataset(tokenized_datasets['train'])\n",
    "val_dataset = torch_dataset(tokenized_datasets['val'])\n",
    "test_dataset = torch_dataset(tokenized_datasets['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "57d3951b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7fa9701569cb45f9a176a5b96c79a1fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating json from Arrow format:   0%|          | 0/5 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "454180f7bb7843d2b480069facf4e004",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating json from Arrow format:   0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2e63dab4f9c4c60877d56d61f4761e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating json from Arrow format:   0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "32655572"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.to_json(\"train_dataset.json\")\n",
    "val_dataset.to_json(\"val_dataset.json\")\n",
    "test_dataset.to_json(\"test_dataset.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30e1afbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3815659a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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