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  1. create-datasets.ipynb +1151 -0
create-datasets.ipynb ADDED
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1
+ {
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "id": "058cad15-13fc-470a-8cbf-38ba779e8b44",
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+ "metadata": {},
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+ "source": [
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+ "## Imports"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 10,
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+ "id": "76fccd62-f056-442c-9115-ec606fa2ec8b",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import pandas as pd\n",
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+ "from datasets import load_dataset"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "994b7360-11b8-4287-be08-455d9657fb08",
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+ "metadata": {},
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+ "source": [
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+ "## Download data"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "id": "24c937d0-ef81-47be-9115-630fdf30ae2c",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "--2022-04-16 11:57:39-- https://zenodo.org/record/2603256/files/train.h5\n",
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+ "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
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+ "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
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+ "HTTP request sent, awaiting response... 200 OK\n",
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+ "Length: 1038496555 (990M) [application/octet-stream]\n",
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+ "Saving to: ‘train.h5’\n",
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+ "\n",
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+ "train.h5 100%[===================>] 990.39M 50.5MB/s in 20s \n",
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+ "\n",
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+ "2022-04-16 11:57:59 (50.8 MB/s) - ‘train.h5’ saved [1038496555/1038496555]\n",
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+ "\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "!wget -O train.h5 https://zenodo.org/record/2603256/files/train.h5"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "id": "4a37ee28-fb23-4ed9-b0dd-c9bfdfcd516c",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "--2022-04-16 11:58:03-- https://zenodo.org/record/2603256/files/val.h5\n",
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+ "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
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+ "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
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+ "HTTP request sent, awaiting response... 200 OK\n",
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+ "Length: 347378076 (331M) [application/octet-stream]\n",
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+ "Saving to: ‘validation.h5’\n",
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+ "\n",
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+ "validation.h5 100%[===================>] 331.29M 41.8MB/s in 9.5s \n",
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+ "\n",
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+ "2022-04-16 11:58:13 (34.7 MB/s) - ‘validation.h5’ saved [347378076/347378076]\n",
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+ "\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "!wget -O validation.h5 https://zenodo.org/record/2603256/files/val.h5"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "id": "d7ddd1a1-b452-4504-a04b-b5f8a8bcf61d",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "--2022-04-16 11:58:15-- https://zenodo.org/record/2603256/files/test.h5\n",
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+ "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
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+ "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
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+ "HTTP request sent, awaiting response... 200 OK\n",
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+ "Length: 347849376 (332M) [application/octet-stream]\n",
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+ "Saving to: ‘test.h5’\n",
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+ "\n",
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+ "test.h5 100%[===================>] 331.73M 14.9MB/s in 21s \n",
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+ "\n",
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+ "2022-04-16 11:58:37 (15.9 MB/s) - ‘test.h5’ saved [347849376/347849376]\n",
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+ "\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "!wget -O test.h5 https://zenodo.org/record/2603256/files/test.h5"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "58c66318-07f9-442e-bc8a-3496fe20ab5a",
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+ "metadata": {},
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+ "source": [
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+ "## Convert to parquet"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 8,
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+ "id": "1a06b45e-a7b8-4bd0-859a-46de3cf90081",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def convert_to_parquet(split):\n",
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+ " df = pd.read_hdf(f\"{split}.h5\", key=\"table\")\n",
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+ " df.to_parquet(f\"{split}.parquet\", index=False)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 9,
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+ "id": "3e1a0cdc-436f-495a-96e7-ae1eda7ceaae",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "for split in [\"train\", \"validation\", \"test\"]:\n",
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+ " convert_to_parquet(split)"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "48696caa-7f47-43ef-8056-9ff45ec9c8d2",
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+ "metadata": {
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+ "tags": []
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+ },
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+ "source": [
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+ "## Check loading script"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 12,
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+ "id": "1e4e06f8-c5cf-49e1-b932-833e3dd85b03",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "Using custom data configuration dl4phys--top_quark_tagging-d8129c455ccac2e9\n"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Downloading and preparing dataset parquet/dl4phys--top_quark_tagging to /Users/lewtun/.cache/huggingface/datasets/parquet/dl4phys--top_quark_tagging-d8129c455ccac2e9/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901...\n"
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+ ]
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "6477d7e388124b728dc9b4702c2ca612",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Downloading data files: 0%| | 0/3 [00:00<?, ?it/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "3d6d524273b246aeb8bc3c70a4e00ad9",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Downloading data: 0%| | 0.00/1.28G [00:00<?, ?B/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "fa9e68e401b840f19849c274f9b3e563",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Downloading data: 0%| | 0.00/509M [00:00<?, ?B/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "22e5f87e05b240308d6badc7f66ea62f",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Downloading data: 0%| | 0.00/508M [00:00<?, ?B/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "bb0e92a6386846eda5af7c056fe74dd4",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Extracting data files: 0%| | 0/3 [00:00<?, ?it/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Dataset parquet downloaded and prepared to /Users/lewtun/.cache/huggingface/datasets/parquet/dl4phys--top_quark_tagging-d8129c455ccac2e9/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901. Subsequent calls will reuse this data.\n"
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+ ]
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "e28bae800f2745a5a415e3cd65431ec2",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ " 0%| | 0/3 [00:00<?, ?it/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
267
+ "data": {
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+ "text/plain": [
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+ "DatasetDict({\n",
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+ " train: Dataset({\n",
271
+ " features: ['E_0', 'PX_0', 'PY_0', 'PZ_0', 'E_1', 'PX_1', 'PY_1', 'PZ_1', 'E_2', 'PX_2', 'PY_2', 'PZ_2', 'E_3', 'PX_3', 'PY_3', 'PZ_3', 'E_4', 'PX_4', 'PY_4', 'PZ_4', 'E_5', 'PX_5', 'PY_5', 'PZ_5', 'E_6', 'PX_6', 'PY_6', 'PZ_6', 'E_7', 'PX_7', 'PY_7', 'PZ_7', 'E_8', 'PX_8', 'PY_8', 'PZ_8', 'E_9', 'PX_9', 'PY_9', 'PZ_9', 'E_10', 'PX_10', 'PY_10', 'PZ_10', 'E_11', 'PX_11', 'PY_11', 'PZ_11', 'E_12', 'PX_12', 'PY_12', 'PZ_12', 'E_13', 'PX_13', 'PY_13', 'PZ_13', 'E_14', 'PX_14', 'PY_14', 'PZ_14', 'E_15', 'PX_15', 'PY_15', 'PZ_15', 'E_16', 'PX_16', 'PY_16', 'PZ_16', 'E_17', 'PX_17', 'PY_17', 'PZ_17', 'E_18', 'PX_18', 'PY_18', 'PZ_18', 'E_19', 'PX_19', 'PY_19', 'PZ_19', 'E_20', 'PX_20', 'PY_20', 'PZ_20', 'E_21', 'PX_21', 'PY_21', 'PZ_21', 'E_22', 'PX_22', 'PY_22', 'PZ_22', 'E_23', 'PX_23', 'PY_23', 'PZ_23', 'E_24', 'PX_24', 'PY_24', 'PZ_24', 'E_25', 'PX_25', 'PY_25', 'PZ_25', 'E_26', 'PX_26', 'PY_26', 'PZ_26', 'E_27', 'PX_27', 'PY_27', 'PZ_27', 'E_28', 'PX_28', 'PY_28', 'PZ_28', 'E_29', 'PX_29', 'PY_29', 'PZ_29', 'E_30', 'PX_30', 'PY_30', 'PZ_30', 'E_31', 'PX_31', 'PY_31', 'PZ_31', 'E_32', 'PX_32', 'PY_32', 'PZ_32', 'E_33', 'PX_33', 'PY_33', 'PZ_33', 'E_34', 'PX_34', 'PY_34', 'PZ_34', 'E_35', 'PX_35', 'PY_35', 'PZ_35', 'E_36', 'PX_36', 'PY_36', 'PZ_36', 'E_37', 'PX_37', 'PY_37', 'PZ_37', 'E_38', 'PX_38', 'PY_38', 'PZ_38', 'E_39', 'PX_39', 'PY_39', 'PZ_39', 'E_40', 'PX_40', 'PY_40', 'PZ_40', 'E_41', 'PX_41', 'PY_41', 'PZ_41', 'E_42', 'PX_42', 'PY_42', 'PZ_42', 'E_43', 'PX_43', 'PY_43', 'PZ_43', 'E_44', 'PX_44', 'PY_44', 'PZ_44', 'E_45', 'PX_45', 'PY_45', 'PZ_45', 'E_46', 'PX_46', 'PY_46', 'PZ_46', 'E_47', 'PX_47', 'PY_47', 'PZ_47', 'E_48', 'PX_48', 'PY_48', 'PZ_48', 'E_49', 'PX_49', 'PY_49', 'PZ_49', 'E_50', 'PX_50', 'PY_50', 'PZ_50', 'E_51', 'PX_51', 'PY_51', 'PZ_51', 'E_52', 'PX_52', 'PY_52', 'PZ_52', 'E_53', 'PX_53', 'PY_53', 'PZ_53', 'E_54', 'PX_54', 'PY_54', 'PZ_54', 'E_55', 'PX_55', 'PY_55', 'PZ_55', 'E_56', 'PX_56', 'PY_56', 'PZ_56', 'E_57', 'PX_57', 'PY_57', 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'PX_86', 'PY_86', 'PZ_86', 'E_87', 'PX_87', 'PY_87', 'PZ_87', 'E_88', 'PX_88', 'PY_88', 'PZ_88', 'E_89', 'PX_89', 'PY_89', 'PZ_89', 'E_90', 'PX_90', 'PY_90', 'PZ_90', 'E_91', 'PX_91', 'PY_91', 'PZ_91', 'E_92', 'PX_92', 'PY_92', 'PZ_92', 'E_93', 'PX_93', 'PY_93', 'PZ_93', 'E_94', 'PX_94', 'PY_94', 'PZ_94', 'E_95', 'PX_95', 'PY_95', 'PZ_95', 'E_96', 'PX_96', 'PY_96', 'PZ_96', 'E_97', 'PX_97', 'PY_97', 'PZ_97', 'E_98', 'PX_98', 'PY_98', 'PZ_98', 'E_99', 'PX_99', 'PY_99', 'PZ_99', 'E_100', 'PX_100', 'PY_100', 'PZ_100', 'E_101', 'PX_101', 'PY_101', 'PZ_101', 'E_102', 'PX_102', 'PY_102', 'PZ_102', 'E_103', 'PX_103', 'PY_103', 'PZ_103', 'E_104', 'PX_104', 'PY_104', 'PZ_104', 'E_105', 'PX_105', 'PY_105', 'PZ_105', 'E_106', 'PX_106', 'PY_106', 'PZ_106', 'E_107', 'PX_107', 'PY_107', 'PZ_107', 'E_108', 'PX_108', 'PY_108', 'PZ_108', 'E_109', 'PX_109', 'PY_109', 'PZ_109', 'E_110', 'PX_110', 'PY_110', 'PZ_110', 'E_111', 'PX_111', 'PY_111', 'PZ_111', 'E_112', 'PX_112', 'PY_112', 'PZ_112', 'E_113', 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272
+ " num_rows: 1211000\n",
273
+ " })\n",
274
+ " test: Dataset({\n",
275
+ " features: ['E_0', 'PX_0', 'PY_0', 'PZ_0', 'E_1', 'PX_1', 'PY_1', 'PZ_1', 'E_2', 'PX_2', 'PY_2', 'PZ_2', 'E_3', 'PX_3', 'PY_3', 'PZ_3', 'E_4', 'PX_4', 'PY_4', 'PZ_4', 'E_5', 'PX_5', 'PY_5', 'PZ_5', 'E_6', 'PX_6', 'PY_6', 'PZ_6', 'E_7', 'PX_7', 'PY_7', 'PZ_7', 'E_8', 'PX_8', 'PY_8', 'PZ_8', 'E_9', 'PX_9', 'PY_9', 'PZ_9', 'E_10', 'PX_10', 'PY_10', 'PZ_10', 'E_11', 'PX_11', 'PY_11', 'PZ_11', 'E_12', 'PX_12', 'PY_12', 'PZ_12', 'E_13', 'PX_13', 'PY_13', 'PZ_13', 'E_14', 'PX_14', 'PY_14', 'PZ_14', 'E_15', 'PX_15', 'PY_15', 'PZ_15', 'E_16', 'PX_16', 'PY_16', 'PZ_16', 'E_17', 'PX_17', 'PY_17', 'PZ_17', 'E_18', 'PX_18', 'PY_18', 'PZ_18', 'E_19', 'PX_19', 'PY_19', 'PZ_19', 'E_20', 'PX_20', 'PY_20', 'PZ_20', 'E_21', 'PX_21', 'PY_21', 'PZ_21', 'E_22', 'PX_22', 'PY_22', 'PZ_22', 'E_23', 'PX_23', 'PY_23', 'PZ_23', 'E_24', 'PX_24', 'PY_24', 'PZ_24', 'E_25', 'PX_25', 'PY_25', 'PZ_25', 'E_26', 'PX_26', 'PY_26', 'PZ_26', 'E_27', 'PX_27', 'PY_27', 'PZ_27', 'E_28', 'PX_28', 'PY_28', 'PZ_28', 'E_29', 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276
+ " num_rows: 404000\n",
277
+ " })\n",
278
+ " validation: Dataset({\n",
279
+ " features: ['E_0', 'PX_0', 'PY_0', 'PZ_0', 'E_1', 'PX_1', 'PY_1', 'PZ_1', 'E_2', 'PX_2', 'PY_2', 'PZ_2', 'E_3', 'PX_3', 'PY_3', 'PZ_3', 'E_4', 'PX_4', 'PY_4', 'PZ_4', 'E_5', 'PX_5', 'PY_5', 'PZ_5', 'E_6', 'PX_6', 'PY_6', 'PZ_6', 'E_7', 'PX_7', 'PY_7', 'PZ_7', 'E_8', 'PX_8', 'PY_8', 'PZ_8', 'E_9', 'PX_9', 'PY_9', 'PZ_9', 'E_10', 'PX_10', 'PY_10', 'PZ_10', 'E_11', 'PX_11', 'PY_11', 'PZ_11', 'E_12', 'PX_12', 'PY_12', 'PZ_12', 'E_13', 'PX_13', 'PY_13', 'PZ_13', 'E_14', 'PX_14', 'PY_14', 'PZ_14', 'E_15', 'PX_15', 'PY_15', 'PZ_15', 'E_16', 'PX_16', 'PY_16', 'PZ_16', 'E_17', 'PX_17', 'PY_17', 'PZ_17', 'E_18', 'PX_18', 'PY_18', 'PZ_18', 'E_19', 'PX_19', 'PY_19', 'PZ_19', 'E_20', 'PX_20', 'PY_20', 'PZ_20', 'E_21', 'PX_21', 'PY_21', 'PZ_21', 'E_22', 'PX_22', 'PY_22', 'PZ_22', 'E_23', 'PX_23', 'PY_23', 'PZ_23', 'E_24', 'PX_24', 'PY_24', 'PZ_24', 'E_25', 'PX_25', 'PY_25', 'PZ_25', 'E_26', 'PX_26', 'PY_26', 'PZ_26', 'E_27', 'PX_27', 'PY_27', 'PZ_27', 'E_28', 'PX_28', 'PY_28', 'PZ_28', 'E_29', 'PX_29', 'PY_29', 'PZ_29', 'E_30', 'PX_30', 'PY_30', 'PZ_30', 'E_31', 'PX_31', 'PY_31', 'PZ_31', 'E_32', 'PX_32', 'PY_32', 'PZ_32', 'E_33', 'PX_33', 'PY_33', 'PZ_33', 'E_34', 'PX_34', 'PY_34', 'PZ_34', 'E_35', 'PX_35', 'PY_35', 'PZ_35', 'E_36', 'PX_36', 'PY_36', 'PZ_36', 'E_37', 'PX_37', 'PY_37', 'PZ_37', 'E_38', 'PX_38', 'PY_38', 'PZ_38', 'E_39', 'PX_39', 'PY_39', 'PZ_39', 'E_40', 'PX_40', 'PY_40', 'PZ_40', 'E_41', 'PX_41', 'PY_41', 'PZ_41', 'E_42', 'PX_42', 'PY_42', 'PZ_42', 'E_43', 'PX_43', 'PY_43', 'PZ_43', 'E_44', 'PX_44', 'PY_44', 'PZ_44', 'E_45', 'PX_45', 'PY_45', 'PZ_45', 'E_46', 'PX_46', 'PY_46', 'PZ_46', 'E_47', 'PX_47', 'PY_47', 'PZ_47', 'E_48', 'PX_48', 'PY_48', 'PZ_48', 'E_49', 'PX_49', 'PY_49', 'PZ_49', 'E_50', 'PX_50', 'PY_50', 'PZ_50', 'E_51', 'PX_51', 'PY_51', 'PZ_51', 'E_52', 'PX_52', 'PY_52', 'PZ_52', 'E_53', 'PX_53', 'PY_53', 'PZ_53', 'E_54', 'PX_54', 'PY_54', 'PZ_54', 'E_55', 'PX_55', 'PY_55', 'PZ_55', 'E_56', 'PX_56', 'PY_56', 'PZ_56', 'E_57', 'PX_57', 'PY_57', 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280
+ " num_rows: 403000\n",
281
+ " })\n",
282
+ "})"
283
+ ]
284
+ },
285
+ "execution_count": 12,
286
+ "metadata": {},
287
+ "output_type": "execute_result"
288
+ }
289
+ ],
290
+ "source": [
291
+ "ds = load_dataset(\"dl4phys/top_quark_tagging\")\n",
292
+ "ds"
293
+ ]
294
+ },
295
+ {
296
+ "cell_type": "code",
297
+ "execution_count": 13,
298
+ "id": "5cff9490-ed63-4590-8e2e-cd9cfe6f02c5",
299
+ "metadata": {},
300
+ "outputs": [
301
+ {
302
+ "data": {
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+ "text/plain": [
304
+ "{'E_0': 474.0711364746094,\n",
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