Upload Data-Prep.ipynb with huggingface_hub

Data-Prep.ipynb

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+{
+ "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",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python-3-9",
+   "language": "python",
+   "name": "python-3-9"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "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
+}