{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "200ebff3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: TOKENIZERS_PARALLELISM=false\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import json\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from random import choice, shuffle\n",
    "import re\n",
    "from textwrap import TextWrapper\n",
    "from time import sleep\n",
    "from tqdm import tqdm\n",
    "\n",
    "def dump(data): print(json.dumps(data, indent=4, sort_keys=True))\n",
    "\n",
    "def print_long(text, width=140, indent=0, initial_indent=0, ofp=None):\n",
    "    wrapper = TextWrapper(initial_indent=\" \" * initial_indent, subsequent_indent=\" \" * indent, width=width)\n",
    "    if ofp is None: print(\"\\n\".join(wrapper.wrap(text)))\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "\n",
    "%env TOKENIZERS_PARALLELISM=false\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "load_dotenv(\"/home/ec2-user/.env\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97c78754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "845f3b7b8d5448b9869e73c1c9c1e840",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1835f0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from umap import UMAP\n",
    "\n",
    "\n",
    "#model = SentenceTransformer('all-MiniLM-L6-v2')\n",
    "model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\n",
    "embeddings = model.encode(texts, show_progress_bar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f2b18a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "texts = TYPE_DF.sentence.values\n",
    "\n",
    "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "embeddings = model.encode(texts, batch_size=BATCH_SIZE, show_progress_bar=True)\n",
    "\n",
    "reducer = UMAP(metric='cosine')\n",
    "embeddings_2d = reducer.fit_transform(embeddings)\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "\n",
    "plt.title(f'UMAP Projected Embeddings of {len(texts)} Has-Nationality relationship type evidence')\n",
    "plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], s=0.1, alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2a1bbd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data-Prep.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d93c5eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import HfApi\n",
    "api = HfApi()\n",
    "api.upload_file(\n",
    "    path_or_fileobj=\"./Data-Prep.ipynb\",\n",
    "    path_in_repo=\"Data-Prep.ipynb\",\n",
    "    repo_id=\"username/test-dataset\",\n",
    "    repo_type=\"dataset\",\n",
    ")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "python-3-9",
   "language": "python",
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}