{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9dbad513",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/firouzi/embedding_model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Downloading readme: 100%|██████████| 419/419 [00:00<00:00, 1.18MB/s]\n",
      "Downloading data: 100%|██████████| 1.59M/1.59M [00:01<00:00, 1.03MB/s]\n",
      "Generating train split: 100%|██████████| 7000/7000 [00:00<00:00, 175360.77 examples/s]\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "ds = load_dataset(\"virattt/financial-qa-10K\", split=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7330f385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'query': 'What area did NVIDIA initially focus on before expanding to other computationally intensive fields?',\n",
       " 'pos': 'Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.',\n",
       " 'id': '0'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = ds.select_columns(column_names=[\"question\", \"context\"])\n",
    "ds = ds.rename_column(\"question\", \"query\")\n",
    "ds = ds.rename_column(\"context\", \"pos\")\n",
    "ds = ds.add_column(\"id\", [str(i) for i in range(len(ds))])\n",
    "ds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ba361dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████| 7000/7000 [00:00<00:00, 19176.72 examples/s]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(520)\n",
    "neg_num = 10\n",
    "\n",
    "def str_to_lst(data):\n",
    "    data[\"pos\"] = [data[\"pos\"]]\n",
    "    return data\n",
    "\n",
    "# sample negative texts\n",
    "new_col = []\n",
    "for i in range(len(ds)):\n",
    "    ids = np.random.randint(0, len(ds), size=neg_num)\n",
    "    while i in ids:\n",
    "        ids = np.random.randint(0, len(ds), size=neg_num)\n",
    "    neg = [ds[i.item()][\"pos\"] for i in ids]\n",
    "    new_col.append(neg)\n",
    "ds = ds.add_column(\"neg\", new_col)\n",
    "\n",
    "# change the key of 'pos' to a list\n",
    "ds = ds.map(str_to_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf3241ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "instruction = \"Represent this sentence for searching relevant passages: \"\n",
    "ds = ds.add_column(\"prompt\", [instruction]*len(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a35c1466",
   "metadata": {},
   "outputs": [],
   "source": [
    "split = ds.train_test_split(test_size=0.1, shuffle=True, seed=520)\n",
    "train = split[\"train\"]\n",
    "test = split[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24f3f7fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating json from Arrow format: 100%|██████████| 7/7 [00:00<00:00, 26.22ba/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "16583481"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.to_json(\"training.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5cc42ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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