Datasets:

siavava
/

ai-tech-articles

+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

.gitignore ADDED Viewed

	@@ -0,0 +1,9 @@

+*.csv
+.DS_Store
+.total
+all
+dictionary
+*.txt
+trash
+*.py
+urls

README.md ADDED Viewed

	@@ -0,0 +1,50 @@

+---
+language:
+  - en
+pretty_name: "AI/Technology Articles"
+tags:
+  - temporal series data
+  - language data
+license: mit
+task_categories:
+  - text-generation
+  - feature-extraction
+---
+# AI/Tech Dataset
+This dataset is a collection of AI/tech articles scraped from the web.
+It's hosted on [HuggingFace Datasets](https://huggingface.co/datasets/siavava/ai-tech-articles), so it is easier to load in and work with.
+## To load the dataset
+### 1. Install [HuggingFace Datasets](https://huggingface.co/docs/datasets/installation.html)
+```bash
+pip install datasets
+```
+### 2. Load the dataset
+```python
+from datasets import load_dataset
+dataset = load_dataset("siavava/ai-tech-articles")
+# optionally, convert it to a pandas dataframe:
+df = dataset["train"].to_pandas()
+```
+> [!NOTE]
+> You do not need to clone this repo.
+>
+> HuggingFace will download the dataset for you, the first time that you load it,
+> and cache it so it does not need to re-download it again
+> (unless it detects a change upstream).
+## File Structure
+- [analytics.ipynb](analytics.ipynb) - Notebook containing some details about the dataset and how to load it.
+- [data/index.parquet](./index.csv) - compressed [parquet](https://www.databricks.com/glossary/what-is-parquet) containing the data.
+- For raw text files, see the [scraper repo](https://github.com/siavava/scrape.hs) on GitHub.

analytics.ipynb ADDED Viewed

	@@ -0,0 +1,208 @@

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Analytics for the Corpus\n",
+    "\n",
+    "## Author: Amittai Siavava"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the CSV metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from datasets import load_dataset\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from collections import Counter\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>year</th>\n",
+       "      <th>title</th>\n",
+       "      <th>url</th>\n",
+       "      <th>text</th>\n",
+       "      <th>__index_level_0__</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>\"MIT Technology Review\"</td>\n",
+       "      <td>\"https://www.technologyreview.com\"</td>\n",
+       "      <td>\"Featured Topics Newsletters Events Podcasts F...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>\"WIRED - The Latest in Technology, Science, Cu...</td>\n",
+       "      <td>\"https://www.wired.com\"</td>\n",
+       "      <td>\"Open Navigation Menu To revisit this article,...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>\"The Verge\"</td>\n",
+       "      <td>\"https://www.theverge.com\"</td>\n",
+       "      <td>\"The Verge homepage The Verge The Verge logo.\\...</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>\"TechCrunch | Startup and Technology News\"</td>\n",
+       "      <td>\"https://www.techcrunch.com\"</td>\n",
+       "      <td>\"WeWork reportedly on the verge of filing for ...</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>\"A new vision of artificial intelligence for t...</td>\n",
+       "      <td>\"https://www.technologyreview.com/2022/04/22/1...</td>\n",
+       "      <td>\"Featured Topics Newsletters Events Podcasts A...</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id    year                                              title  \\\n",
+       "0   0  2023.0                            \"MIT Technology Review\"   \n",
+       "1   1  2023.0  \"WIRED - The Latest in Technology, Science, Cu...   \n",
+       "2   2  2019.0                                        \"The Verge\"   \n",
+       "3   3  2023.0         \"TechCrunch | Startup and Technology News\"   \n",
+       "4   4  2022.0  \"A new vision of artificial intelligence for t...   \n",
+       "\n",
+       "                                                 url  \\\n",
+       "0                 \"https://www.technologyreview.com\"   \n",
+       "1                            \"https://www.wired.com\"   \n",
+       "2                         \"https://www.theverge.com\"   \n",
+       "3                       \"https://www.techcrunch.com\"   \n",
+       "4  \"https://www.technologyreview.com/2022/04/22/1...   \n",
+       "\n",
+       "                                                text  __index_level_0__  \n",
+       "0  \"Featured Topics Newsletters Events Podcasts F...                  0  \n",
+       "1  \"Open Navigation Menu To revisit this article,...                  1  \n",
+       "2  \"The Verge homepage The Verge The Verge logo.\\...                  2  \n",
+       "3  \"WeWork reportedly on the verge of filing for ...                  3  \n",
+       "4  \"Featured Topics Newsletters Events Podcasts A...                  4  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = load_dataset(\"siavava/ai-tech-articles\")\n",
+    "# convert to pandas, use id as index\n",
+    "df = dataset[\"train\"].to_pandas()\n",
+    "df.head(5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = np.array(df)\n",
+    "years = data[:, 1]\n",
+    "\n",
+    "# get unique years\n",
+    "unique_years = np.unique(years)\n",
+    "\n",
+    "# get counts\n",
+    "counts = np.array([np.sum(years == year) for year in unique_years])\n",
+    "\n",
+    "plt.bar(unique_years, counts, label=\"Total\")\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "607b7d84c7d8e26dbbffb4014e40424fe2faf80a09a85d717e93e42c2773dc40"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('ml')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

counts.csv ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:06a4a5ec4a4ac58a3f578ae83ad05450730de0a7700dab86e0ce8c21c60dde9f
+size 535

test.ipynb ADDED Viewed

	@@ -0,0 +1,258 @@

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# This is a test-script that loads the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install datasets\n",
+    "from datasets import load_dataset\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b56274faa04d46c0a8ce3871242ffc6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/1.37k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "Couldn't find a dataset script at /Users/amittaijoel/workspace/crawl.hs/data/metadata/siavava/ai-tech-articles/ai-tech-articles.py or any data file in the same directory. Couldn't find 'siavava/ai-tech-articles' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in siavava/ai-tech-articles. ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m/Users/amittaijoel/workspace/crawl.hs/data/metadata/test.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/amittaijoel/workspace/crawl.hs/data/metadata/test.ipynb#W2sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m dt \u001b[39m=\u001b[39m load_dataset(\u001b[39m\"\u001b[39;49m\u001b[39msiavava/ai-tech-articles\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
+      "File \u001b[0;32m~/miniconda3/envs/data-mining/lib/python3.11/site-packages/datasets/load.py:2129\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m   2124\u001b[0m verification_mode \u001b[39m=\u001b[39m VerificationMode(\n\u001b[1;32m   2125\u001b[0m     (verification_mode \u001b[39mor\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mBASIC_CHECKS) \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m save_infos \u001b[39melse\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mALL_CHECKS\n\u001b[1;32m   2126\u001b[0m )\n\u001b[1;32m   2128\u001b[0m \u001b[39m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 2129\u001b[0m builder_instance \u001b[39m=\u001b[39m load_dataset_builder(\n\u001b[1;32m   2130\u001b[0m     path\u001b[39m=\u001b[39;49mpath,\n\u001b[1;32m   2131\u001b[0m     name\u001b[39m=\u001b[39;49mname,\n\u001b[1;32m   2132\u001b[0m     data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m   2133\u001b[0m     data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m   2134\u001b[0m     cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[1;32m   2135\u001b[0m     features\u001b[39m=\u001b[39;49mfeatures,\n\u001b[1;32m   2136\u001b[0m     download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   2137\u001b[0m     download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   2138\u001b[0m     revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   2139\u001b[0m     token\u001b[39m=\u001b[39;49mtoken,\n\u001b[1;32m   2140\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m   2141\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mconfig_kwargs,\n\u001b[1;32m   2142\u001b[0m )\n\u001b[1;32m   2144\u001b[0m \u001b[39m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m   2145\u001b[0m \u001b[39mif\u001b[39;00m streaming:\n",
+      "File \u001b[0;32m~/miniconda3/envs/data-mining/lib/python3.11/site-packages/datasets/load.py:1815\u001b[0m, in \u001b[0;36mload_dataset_builder\u001b[0;34m(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m   1813\u001b[0m     download_config \u001b[39m=\u001b[39m download_config\u001b[39m.\u001b[39mcopy() \u001b[39mif\u001b[39;00m download_config \u001b[39melse\u001b[39;00m DownloadConfig()\n\u001b[1;32m   1814\u001b[0m     download_config\u001b[39m.\u001b[39mstorage_options\u001b[39m.\u001b[39mupdate(storage_options)\n\u001b[0;32m-> 1815\u001b[0m dataset_module \u001b[39m=\u001b[39m dataset_module_factory(\n\u001b[1;32m   1816\u001b[0m     path,\n\u001b[1;32m   1817\u001b[0m     revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   1818\u001b[0m     download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   1819\u001b[0m     download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   1820\u001b[0m     data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m   1821\u001b[0m     data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m   1822\u001b[0m )\n\u001b[1;32m   1823\u001b[0m \u001b[39m# Get dataset builder class from the processing script\u001b[39;00m\n\u001b[1;32m   1824\u001b[0m builder_kwargs \u001b[39m=\u001b[39m dataset_module\u001b[39m.\u001b[39mbuilder_kwargs\n",
+      "File \u001b[0;32m~/miniconda3/envs/data-mining/lib/python3.11/site-packages/datasets/load.py:1508\u001b[0m, in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)\u001b[0m\n\u001b[1;32m   1506\u001b[0m                 \u001b[39mraise\u001b[39;00m e1 \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1507\u001b[0m             \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(e1, \u001b[39mFileNotFoundError\u001b[39;00m):\n\u001b[0;32m-> 1508\u001b[0m                 \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1509\u001b[0m                     \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find a dataset script at \u001b[39m\u001b[39m{\u001b[39;00mrelative_to_absolute_path(combined_path)\u001b[39m}\u001b[39;00m\u001b[39m or any data file in the same directory. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1510\u001b[0m                     \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mpath\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m on the Hugging Face Hub either: \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(e1)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m: \u001b[39m\u001b[39m{\u001b[39;00me1\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1511\u001b[0m                 ) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1512\u001b[0m             \u001b[39mraise\u001b[39;00m e1 \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1513\u001b[0m \u001b[39melse\u001b[39;00m:\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: Couldn't find a dataset script at /Users/amittaijoel/workspace/crawl.hs/data/metadata/siavava/ai-tech-articles/ai-tech-articles.py or any data file in the same directory. Couldn't find 'siavava/ai-tech-articles' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in siavava/ai-tech-articles. "
+     ]
+    }
+   ],
+   "source": [
+    "dt = load_dataset(\"siavava/ai-tech-articles\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>year</th>\n",
+       "      <th>title</th>\n",
+       "      <th>url</th>\n",
+       "      <th>text</th>\n",
+       "      <th>__index_level_0__</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>\"MIT Technology Review\"</td>\n",
+       "      <td>\"https://www.technologyreview.com\"</td>\n",
+       "      <td>\"Featured Topics Newsletters Events Podcasts F...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>\"WIRED - The Latest in Technology, Science, Cu...</td>\n",
+       "      <td>\"https://www.wired.com\"</td>\n",
+       "      <td>\"Open Navigation Menu To revisit this article,...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>\"The Verge\"</td>\n",
+       "      <td>\"https://www.theverge.com\"</td>\n",
+       "      <td>\"The Verge homepage The Verge The Verge logo.\\...</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>\"TechCrunch | Startup and Technology News\"</td>\n",
+       "      <td>\"https://www.techcrunch.com\"</td>\n",
+       "      <td>\"WeWork reportedly on the verge of filing for ...</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>\"A new vision of artificial intelligence for t...</td>\n",
+       "      <td>\"https://www.technologyreview.com/2022/04/22/1...</td>\n",
+       "      <td>\"Featured Topics Newsletters Events Podcasts A...</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>\"The scientist who co-created CRISPR isn’t rul...</td>\n",
+       "      <td>\"https://www.technologyreview.com/2022/04/26/1...</td>\n",
+       "      <td>\"Featured Topics Newsletters Events Podcasts F...</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>\"These fast, cheap tests could help us coexist...</td>\n",
+       "      <td>\"https://www.technologyreview.com/2022/04/27/1...</td>\n",
+       "      <td>\"Featured Topics Newsletters Events Podcasts F...</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>\"Tackling multiple tasks with a single visual ...</td>\n",
+       "      <td>\"http://www.deepmind.com/blog/tackling-multipl...</td>\n",
+       "      <td>\"DeepMind Search Search Close DeepMind About O...</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>\"About - Google DeepMind\"</td>\n",
+       "      <td>\"https://www.deepmind.com/about\"</td>\n",
+       "      <td>\"DeepMind Search Search Close DeepMind About O...</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>\"Blog - Google DeepMind\"</td>\n",
+       "      <td>\"https://www.deepmind.com/blog-categories/appl...</td>\n",
+       "      <td>\"DeepMind Search Search Close DeepMind About O...</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id    year                                              title  \\\n",
+       "0   0  2023.0                            \"MIT Technology Review\"   \n",
+       "1   1  2023.0  \"WIRED - The Latest in Technology, Science, Cu...   \n",
+       "2   2  2019.0                                        \"The Verge\"   \n",
+       "3   3  2023.0         \"TechCrunch | Startup and Technology News\"   \n",
+       "4   4  2022.0  \"A new vision of artificial intelligence for t...   \n",
+       "5   5  2022.0  \"The scientist who co-created CRISPR isn’t rul...   \n",
+       "6   6  2022.0  \"These fast, cheap tests could help us coexist...   \n",
+       "7   7  2022.0  \"Tackling multiple tasks with a single visual ...   \n",
+       "8   8  2019.0                          \"About - Google DeepMind\"   \n",
+       "9   9  2023.0                           \"Blog - Google DeepMind\"   \n",
+       "\n",
+       "                                                 url  \\\n",
+       "0                 \"https://www.technologyreview.com\"   \n",
+       "1                            \"https://www.wired.com\"   \n",
+       "2                         \"https://www.theverge.com\"   \n",
+       "3                       \"https://www.techcrunch.com\"   \n",
+       "4  \"https://www.technologyreview.com/2022/04/22/1...   \n",
+       "5  \"https://www.technologyreview.com/2022/04/26/1...   \n",
+       "6  \"https://www.technologyreview.com/2022/04/27/1...   \n",
+       "7  \"http://www.deepmind.com/blog/tackling-multipl...   \n",
+       "8                   \"https://www.deepmind.com/about\"   \n",
+       "9  \"https://www.deepmind.com/blog-categories/appl...   \n",
+       "\n",
+       "                                                text  __index_level_0__  \n",
+       "0  \"Featured Topics Newsletters Events Podcasts F...                  0  \n",
+       "1  \"Open Navigation Menu To revisit this article,...                  1  \n",
+       "2  \"The Verge homepage The Verge The Verge logo.\\...                  2  \n",
+       "3  \"WeWork reportedly on the verge of filing for ...                  3  \n",
+       "4  \"Featured Topics Newsletters Events Podcasts A...                  4  \n",
+       "5  \"Featured Topics Newsletters Events Podcasts F...                  5  \n",
+       "6  \"Featured Topics Newsletters Events Podcasts F...                  6  \n",
+       "7  \"DeepMind Search Search Close DeepMind About O...                  7  \n",
+       "8  \"DeepMind Search Search Close DeepMind About O...                  8  \n",
+       "9  \"DeepMind Search Search Close DeepMind About O...                  9  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = dt[\"train\"].to_pandas()\n",
+    "df.head(10)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "data-mining",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

train.csv ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:1fc6cdf5d4ab2f48a3dd3228d738848703c0b83bb1f656f2b4b48b4e036978cf
+size 125131707