Commit ·
c54b89d
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Parent(s): 61ff60c
Update parquet files
Browse files- .gitignore +0 -160
- README.md +0 -102
- create-datasets.ipynb +0 -1151
- validation.parquet → dl4phys--top_tagging/parquet-test-00000-of-00003.parquet +2 -2
- test.parquet → dl4phys--top_tagging/parquet-test-00001-of-00003.parquet +2 -2
- train.parquet → dl4phys--top_tagging/parquet-test-00002-of-00003.parquet +2 -2
- dl4phys--top_tagging/parquet-train-00000-of-00008.parquet +3 -0
- dl4phys--top_tagging/parquet-train-00001-of-00008.parquet +3 -0
- dl4phys--top_tagging/parquet-train-00002-of-00008.parquet +3 -0
- dl4phys--top_tagging/parquet-train-00003-of-00008.parquet +3 -0
- dl4phys--top_tagging/parquet-train-00004-of-00008.parquet +3 -0
- dl4phys--top_tagging/parquet-train-00005-of-00008.parquet +3 -0
- dl4phys--top_tagging/parquet-train-00006-of-00008.parquet +3 -0
- dl4phys--top_tagging/parquet-train-00007-of-00008.parquet +3 -0
- dl4phys--top_tagging/parquet-validation-00000-of-00003.parquet +3 -0
- dl4phys--top_tagging/parquet-validation-00001-of-00003.parquet +3 -0
- dl4phys--top_tagging/parquet-validation-00002-of-00003.parquet +3 -0
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README.md
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---
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license: cc-by-4.0
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---
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# Dataset Card for Top Quark Tagging
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## Table of Contents
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- [Dataset Card for Top Quark Tagging](#dataset-card-for-top-quark-tagging)
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** https://zenodo.org/record/2603256
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- **Paper:** https://arxiv.org/abs/1902.09914
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- **Point of Contact:** [Gregor Kasieczka](gregor.kasieczka@uni-hamburg.de)
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### Dataset Summary
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Top Quark Tagging is a dataset of Monte Carlo simulated events produced by proton-proton collisions at the Large Hadron Collider. The top-quark signal and mixed quark-gluon background jets are produced with Pythia8 with its default tune for a center-of-mass energy of 14 TeV. Multiple interactions and pile-up are ignored. The leading 200 jet constituent four-momenta \\( (E, p_x, p_y, p_z) \\) are stored, with zero-padding applied to jets with fewer than 200 constituents.
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### Supported Tasks and Leaderboards
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- `tabular-classification`: The dataset can be used to train a model for tabular binary classification, which consists in predicting whether an event is produced from a top signal or quark-gluon background. Success on this task is typically measured by achieving a *high* [accuracy](https://huggingface.co/metrics/accuracy) and AUC score.
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## Dataset Structure
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### Data Instances
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Each instance in the dataset consists of the four-momenta of the leading 200 jet constituents, sorted by \\(p_T\\). For jets with fewer than 200 constituents, zero-padding is applied. The four-momenta of the top-quark are also provided, along with a label in the `is_signal_new` column to indicate whether the event stems from a top-quark (1) or QCD background (0). An example instance looks as follows:
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```
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{'E_0': 474.0711364746094,
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'PX_0': -250.34703063964844,
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'PY_0': -223.65196228027344,
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'PZ_0': -334.73809814453125,
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...
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'E_199': 0.0,
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'PX_199': 0.0,
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'PY_199': 0.0,
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'PZ_199': 0.0,
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'truthE': 0.0,
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'truthPX': 0.0,
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'truthPY': 0.0,
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'truthPZ': 0.0,
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'ttv': 0,
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'is_signal_new': 0}
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```
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### Data Fields
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The fields in the dataset have the following meaning:
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- `E_i`: the energy of jet constituent \\(i\\).
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- `PX_i`: the \\(x\\) component of the jet constituent's momentum
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- `PY_i`: the \\(y\\) component of the jet constituent's momentum
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- `PZ_i`: the \\(z\\) component of the jet constituent's momentum
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- `truthE`: the energy of the top-quark
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- `truthPX`: the \\(x\\) component of the top quark's momentum
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- `truthPY`: the \\(y\\) component of the top quark's momentum
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- `truthPZ`: the \\(z\\) component of the top quark's momentum
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- `ttv`: a flag that indicates which split (train, validation, or test) that a jet belongs to. Redundant since each split is provided as a separate dataset
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- `is_signal_new`: the label for each jet. A 1 indicates a top-quark, while a 0 indicates QCD background.
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### Data Splits
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| | train | validation | test |
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|------------------|--------:|-----------:|-------:|
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| Number of events | 1211000 | 403000 | 404000 |
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### Licensing Information
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This dataset is released under the [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) license.
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### Citation Information
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```
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@dataset{kasieczka_gregor_2019_2603256,
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author = {Kasieczka, Gregor and
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Plehn, Tilman and
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Thompson, Jennifer and
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Russel, Michael},
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title = {Top Quark Tagging Reference Dataset},
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month = mar,
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year = 2019,
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publisher = {Zenodo},
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version = {v0 (2018\_03\_27)},
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doi = {10.5281/zenodo.2603256},
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url = {https://doi.org/10.5281/zenodo.2603256}
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}
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```
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### Contributions
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Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.
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create-datasets.ipynb
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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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"cell_type": "code",
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Download data"
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"cell_type": "code",
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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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"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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"2022-04-16 11:58:13 (34.7 MB/s) - ‘validation.h5’ saved [347378076/347378076]\n",
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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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"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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"Length: 347849376 (332M) [application/octet-stream]\n",
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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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"metadata": {},
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"source": [
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"## Convert to parquet"
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{
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"cell_type": "code",
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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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"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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"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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"cell_type": "code",
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"id": "1e4e06f8-c5cf-49e1-b932-833e3dd85b03",
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"metadata": {},
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"text": [
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"Using custom data configuration dl4phys--top_quark_tagging-d8129c455ccac2e9\n"
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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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"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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"DatasetDict({\n",
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" train: Dataset({\n",
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" 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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| 273 |
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| 274 |
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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', 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'PZ_189', 'E_190', 'PX_190', 'PY_190', 'PZ_190', 'E_191', 'PX_191', 'PY_191', 'PZ_191', 'E_192', 'PX_192', 'PY_192', 'PZ_192', 'E_193', 'PX_193', 'PY_193', 'PZ_193', 'E_194', 'PX_194', 'PY_194', 'PZ_194', 'E_195', 'PX_195', 'PY_195', 'PZ_195', 'E_196', 'PX_196', 'PY_196', 'PZ_196', 'E_197', 'PX_197', 'PY_197', 'PZ_197', 'E_198', 'PX_198', 'PY_198', 'PZ_198', 'E_199', 'PX_199', 'PY_199', 'PZ_199', 'truthE', 'truthPX', 'truthPY', 'truthPZ', 'ttv', 'is_signal_new'],\n",
|
| 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": {
|
| 303 |
-
"text/plain": [
|
| 304 |
-
"{'E_0': 474.0711364746094,\n",
|
| 305 |
-
" 'PX_0': -250.34703063964844,\n",
|
| 306 |
-
" 'PY_0': -223.65196228027344,\n",
|
| 307 |
-
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|
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| 309 |
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| 312 |
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| 313 |
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" 'PX_2': -55.415000915527344,\n",
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| 314 |
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|
| 315 |
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|
| 316 |
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| 317 |
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| 319 |
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| 320 |
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| 348 |
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| 352 |
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| 353 |
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|
| 981 |
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| 982 |
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| 983 |
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| 986 |
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| 987 |
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| 997 |
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| 998 |
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| 999 |
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| 1000 |
-
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| 1001 |
-
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| 1002 |
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| 1003 |
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| 1004 |
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| 1005 |
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| 1006 |
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| 1007 |
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-
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| 1009 |
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| 1010 |
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| 1011 |
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| 1012 |
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| 1013 |
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| 1014 |
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| 1015 |
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| 1016 |
-
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| 1017 |
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| 1018 |
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| 1019 |
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| 1020 |
-
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| 1021 |
-
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| 1022 |
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" 'PY_179': 0.0,\n",
|
| 1023 |
-
" 'PZ_179': 0.0,\n",
|
| 1024 |
-
" 'E_180': 0.0,\n",
|
| 1025 |
-
" 'PX_180': 0.0,\n",
|
| 1026 |
-
" 'PY_180': 0.0,\n",
|
| 1027 |
-
" 'PZ_180': 0.0,\n",
|
| 1028 |
-
" 'E_181': 0.0,\n",
|
| 1029 |
-
" 'PX_181': 0.0,\n",
|
| 1030 |
-
" 'PY_181': 0.0,\n",
|
| 1031 |
-
" 'PZ_181': 0.0,\n",
|
| 1032 |
-
" 'E_182': 0.0,\n",
|
| 1033 |
-
" 'PX_182': 0.0,\n",
|
| 1034 |
-
" 'PY_182': 0.0,\n",
|
| 1035 |
-
" 'PZ_182': 0.0,\n",
|
| 1036 |
-
" 'E_183': 0.0,\n",
|
| 1037 |
-
" 'PX_183': 0.0,\n",
|
| 1038 |
-
" 'PY_183': 0.0,\n",
|
| 1039 |
-
" 'PZ_183': 0.0,\n",
|
| 1040 |
-
" 'E_184': 0.0,\n",
|
| 1041 |
-
" 'PX_184': 0.0,\n",
|
| 1042 |
-
" 'PY_184': 0.0,\n",
|
| 1043 |
-
" 'PZ_184': 0.0,\n",
|
| 1044 |
-
" 'E_185': 0.0,\n",
|
| 1045 |
-
" 'PX_185': 0.0,\n",
|
| 1046 |
-
" 'PY_185': 0.0,\n",
|
| 1047 |
-
" 'PZ_185': 0.0,\n",
|
| 1048 |
-
" 'E_186': 0.0,\n",
|
| 1049 |
-
" 'PX_186': 0.0,\n",
|
| 1050 |
-
" 'PY_186': 0.0,\n",
|
| 1051 |
-
" 'PZ_186': 0.0,\n",
|
| 1052 |
-
" 'E_187': 0.0,\n",
|
| 1053 |
-
" 'PX_187': 0.0,\n",
|
| 1054 |
-
" 'PY_187': 0.0,\n",
|
| 1055 |
-
" 'PZ_187': 0.0,\n",
|
| 1056 |
-
" 'E_188': 0.0,\n",
|
| 1057 |
-
" 'PX_188': 0.0,\n",
|
| 1058 |
-
" 'PY_188': 0.0,\n",
|
| 1059 |
-
" 'PZ_188': 0.0,\n",
|
| 1060 |
-
" 'E_189': 0.0,\n",
|
| 1061 |
-
" 'PX_189': 0.0,\n",
|
| 1062 |
-
" 'PY_189': 0.0,\n",
|
| 1063 |
-
" 'PZ_189': 0.0,\n",
|
| 1064 |
-
" 'E_190': 0.0,\n",
|
| 1065 |
-
" 'PX_190': 0.0,\n",
|
| 1066 |
-
" 'PY_190': 0.0,\n",
|
| 1067 |
-
" 'PZ_190': 0.0,\n",
|
| 1068 |
-
" 'E_191': 0.0,\n",
|
| 1069 |
-
" 'PX_191': 0.0,\n",
|
| 1070 |
-
" 'PY_191': 0.0,\n",
|
| 1071 |
-
" 'PZ_191': 0.0,\n",
|
| 1072 |
-
" 'E_192': 0.0,\n",
|
| 1073 |
-
" 'PX_192': 0.0,\n",
|
| 1074 |
-
" 'PY_192': 0.0,\n",
|
| 1075 |
-
" 'PZ_192': 0.0,\n",
|
| 1076 |
-
" 'E_193': 0.0,\n",
|
| 1077 |
-
" 'PX_193': 0.0,\n",
|
| 1078 |
-
" 'PY_193': 0.0,\n",
|
| 1079 |
-
" 'PZ_193': 0.0,\n",
|
| 1080 |
-
" 'E_194': 0.0,\n",
|
| 1081 |
-
" 'PX_194': 0.0,\n",
|
| 1082 |
-
" 'PY_194': 0.0,\n",
|
| 1083 |
-
" 'PZ_194': 0.0,\n",
|
| 1084 |
-
" 'E_195': 0.0,\n",
|
| 1085 |
-
" 'PX_195': 0.0,\n",
|
| 1086 |
-
" 'PY_195': 0.0,\n",
|
| 1087 |
-
" 'PZ_195': 0.0,\n",
|
| 1088 |
-
" 'E_196': 0.0,\n",
|
| 1089 |
-
" 'PX_196': 0.0,\n",
|
| 1090 |
-
" 'PY_196': 0.0,\n",
|
| 1091 |
-
" 'PZ_196': 0.0,\n",
|
| 1092 |
-
" 'E_197': 0.0,\n",
|
| 1093 |
-
" 'PX_197': 0.0,\n",
|
| 1094 |
-
" 'PY_197': 0.0,\n",
|
| 1095 |
-
" 'PZ_197': 0.0,\n",
|
| 1096 |
-
" 'E_198': 0.0,\n",
|
| 1097 |
-
" 'PX_198': 0.0,\n",
|
| 1098 |
-
" 'PY_198': 0.0,\n",
|
| 1099 |
-
" 'PZ_198': 0.0,\n",
|
| 1100 |
-
" 'E_199': 0.0,\n",
|
| 1101 |
-
" 'PX_199': 0.0,\n",
|
| 1102 |
-
" 'PY_199': 0.0,\n",
|
| 1103 |
-
" 'PZ_199': 0.0,\n",
|
| 1104 |
-
" 'truthE': 0.0,\n",
|
| 1105 |
-
" 'truthPX': 0.0,\n",
|
| 1106 |
-
" 'truthPY': 0.0,\n",
|
| 1107 |
-
" 'truthPZ': 0.0,\n",
|
| 1108 |
-
" 'ttv': 0,\n",
|
| 1109 |
-
" 'is_signal_new': 0}"
|
| 1110 |
-
]
|
| 1111 |
-
},
|
| 1112 |
-
"execution_count": 13,
|
| 1113 |
-
"metadata": {},
|
| 1114 |
-
"output_type": "execute_result"
|
| 1115 |
-
}
|
| 1116 |
-
],
|
| 1117 |
-
"source": [
|
| 1118 |
-
"ds[\"train\"][0]"
|
| 1119 |
-
]
|
| 1120 |
-
},
|
| 1121 |
-
{
|
| 1122 |
-
"cell_type": "code",
|
| 1123 |
-
"execution_count": null,
|
| 1124 |
-
"id": "0a89c061-8e7d-4360-abf8-b377f62c2ff0",
|
| 1125 |
-
"metadata": {},
|
| 1126 |
-
"outputs": [],
|
| 1127 |
-
"source": []
|
| 1128 |
-
}
|
| 1129 |
-
],
|
| 1130 |
-
"metadata": {
|
| 1131 |
-
"kernelspec": {
|
| 1132 |
-
"display_name": "hf",
|
| 1133 |
-
"language": "python",
|
| 1134 |
-
"name": "hf"
|
| 1135 |
-
},
|
| 1136 |
-
"language_info": {
|
| 1137 |
-
"codemirror_mode": {
|
| 1138 |
-
"name": "ipython",
|
| 1139 |
-
"version": 3
|
| 1140 |
-
},
|
| 1141 |
-
"file_extension": ".py",
|
| 1142 |
-
"mimetype": "text/x-python",
|
| 1143 |
-
"name": "python",
|
| 1144 |
-
"nbconvert_exporter": "python",
|
| 1145 |
-
"pygments_lexer": "ipython3",
|
| 1146 |
-
"version": "3.9.7"
|
| 1147 |
-
}
|
| 1148 |
-
},
|
| 1149 |
-
"nbformat": 4,
|
| 1150 |
-
"nbformat_minor": 5
|
| 1151 |
-
}
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|
validation.parquet → dl4phys--top_tagging/parquet-test-00000-of-00003.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
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|
| 3 |
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size
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|
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| 2 |
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|
| 3 |
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size 214662655
|
test.parquet → dl4phys--top_tagging/parquet-test-00001-of-00003.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
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| 3 |
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|
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| 3 |
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size 214754873
|
train.parquet → dl4phys--top_tagging/parquet-test-00002-of-00003.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
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| 3 |
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size 112608922
|
dl4phys--top_tagging/parquet-train-00000-of-00008.parquet
ADDED
|
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|
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|
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|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 214960293
|
dl4phys--top_tagging/parquet-train-00001-of-00008.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 214464266
|
dl4phys--top_tagging/parquet-train-00002-of-00008.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
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|
|
|
|
|
|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 214782647
|
dl4phys--top_tagging/parquet-train-00003-of-00008.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 214922106
|
dl4phys--top_tagging/parquet-train-00004-of-00008.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
|
|
|
|
|
|
|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 214724516
|
dl4phys--top_tagging/parquet-train-00005-of-00008.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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