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--- |
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annotations_creators: [] |
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license: [] |
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pretty_name: tabular_benchmark |
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tags: [] |
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task_categories: |
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- tabular-classification |
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- tabular-regression |
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configs: |
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- config_name: clf_cat_covertype |
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data_files: clf_cat/covertype.csv |
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- config_name: clf_num_Higgs |
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data_files: clf_num/Higgs.csv |
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--- |
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# Tabular Benchmark |
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## Dataset Description |
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This dataset is a curation of various datasets from [openML](https://www.openml.org/) and is curated to benchmark performance of various machine learning algorithms. |
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- **Repository:** https://github.com/LeoGrin/tabular-benchmark/community |
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- **Paper:** https://hal.archives-ouvertes.fr/hal-03723551v2/document |
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### Dataset Summary |
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Benchmark made of curation of various tabular data learning tasks, including: |
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- Regression from Numerical and Categorical Features |
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- Regression from Numerical Features |
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- Classification from Numerical and Categorical Features |
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- Classification from Numerical Features |
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### Supported Tasks and Leaderboards |
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- `tabular-regression` |
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- `tabular-classification` |
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## Dataset Structure |
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### Data Splits |
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This dataset consists of four splits (folders) based on tasks and datasets included in tasks. |
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- reg_num: Task identifier for regression on numerical features. |
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- reg_cat: Task identifier for regression on numerical and categorical features. |
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- clf_num: Task identifier for classification on numerical features. |
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- clf_cat: Task identifier for classification on categorical features. |
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Depending on the dataset you want to load, you can load the dataset by passing `task_name/dataset_name` to `data_files` argument of `load_dataset` like below: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("inria-soda/tabular-benchmark", data_files="reg_cat/house_sales.csv") |
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``` |
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## Dataset Creation |
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### Curation Rationale |
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This dataset is curated to benchmark performance of tree based models against neural networks. The process of picking the datasets for curation is mentioned in the paper as below: |
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- **Heterogeneous columns**. Columns should correspond to features of different nature. This excludes |
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images or signal datasets where each column corresponds to the same signal on different sensors. |
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- **Not high dimensional**. We only keep datasets with a d/n ratio below 1/10. |
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- **Undocumented datasets** We remove datasets where too little information is available. We did keep |
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datasets with hidden column names if it was clear that the features were heterogeneous. |
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- **I.I.D. data**. We remove stream-like datasets or time series. |
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- **Real-world data**. We remove artificial datasets but keep some simulated datasets. The difference is |
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subtle, but we try to keep simulated datasets if learning these datasets are of practical importance |
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(like the Higgs dataset), and not just a toy example to test specific model capabilities. |
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- **Not too small**. We remove datasets with too few features (< 4) and too few samples (< 3 000). For |
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benchmarks on numerical features only, we remove categorical features before checking if enough |
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features and samples are remaining. |
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- **Not too easy**. We remove datasets which are too easy. Specifically, we remove a dataset if a simple model (max of a single tree and a regression, logistic or OLS) |
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reaches a score whose relative difference with the score of both a default Resnet (from Gorishniy et al. [2021]) and a default HistGradientBoosting model (from scikit learn) |
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is below 5%. Other benchmarks use different metrics to remove too easy datasets, like removing datasets perfectly separated by a single decision classifier [Bischl et al., 2021], |
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but this ignores varying Bayes rate across datasets. As tree ensembles are superior to simple trees and logistic regresison [Fernández-Delgado et al., 2014], |
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a close score for the simple and powerful models suggests that we are already close to the best achievable score. |
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- **Not deterministic**. We remove datasets where the target is a deterministic function of the data. This |
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mostly means removing datasets on games like poker and chess. Indeed, we believe that these |
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datasets are very different from most real-world tabular datasets, and should be studied separately |
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### Source Data |
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**Numerical Classification** |
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|dataset_name|n_samples|n_features|original_link|new_link| |
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|---|---|---|---|---| |
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|electricity|38474.0|7.0|https://www.openml.org/d/151|https://www.openml.org/d/44120| |
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|covertype|566602.0|10.0|https://www.openml.org/d/293|https://www.openml.org/d/44121| |
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|pol|10082.0|26.0|https://www.openml.org/d/722|https://www.openml.org/d/44122| |
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|house_16H|13488.0|16.0|https://www.openml.org/d/821|https://www.openml.org/d/44123| |
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|MagicTelescope|13376.0|10.0|https://www.openml.org/d/1120|https://www.openml.org/d/44125| |
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|bank-marketing|10578.0|7.0|https://www.openml.org/d/1461|https://www.openml.org/d/44126| |
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|Bioresponse|3434.0|419.0|https://www.openml.org/d/4134|https://www.openml.org/d/45019| |
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|MiniBooNE|72998.0|50.0|https://www.openml.org/d/41150|https://www.openml.org/d/44128| |
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|default-of-credit-card-clients|13272.0|20.0|https://www.openml.org/d/42477|https://www.openml.org/d/45020| |
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|Higgs|940160.0|24.0|https://www.openml.org/d/42769|https://www.openml.org/d/44129| |
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|eye_movements|7608.0|20.0|https://www.openml.org/d/1044|https://www.openml.org/d/44130| |
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|Diabetes130US|71090.0|7.0|https://www.openml.org/d/4541|https://www.openml.org/d/45022| |
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|jannis|57580.0|54.0|https://www.openml.org/d/41168|https://www.openml.org/d/45021| |
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|heloc|10000.0|22.0|"https://www.kaggle.com/datasets/averkiyoliabev/home-equity-line-of-creditheloc?select=heloc_dataset_v1+%281%29.csv"|https://www.openml.org/d/45026| |
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|credit|16714.0|10.0|"https://www.kaggle.com/c/GiveMeSomeCredit/data?select=cs-training.csv"|https://www.openml.org/d/44089| |
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|california|20634.0|8.0|"https://www.dcc.fc.up.pt/ltorgo/Regression/cal_housing.html"|https://www.openml.org/d/45028| |
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**Categorical Classification** |
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|dataset_name|n_samples|n_features|original_link|new_link| |
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|---|---|---|---|---| |
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|electricity|38474.0|8.0|https://www.openml.org/d/151|https://www.openml.org/d/44156| |
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|eye_movements|7608.0|23.0|https://www.openml.org/d/1044|https://www.openml.org/d/44157| |
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|covertype|423680.0|54.0|https://www.openml.org/d/1596|https://www.openml.org/d/44159| |
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|albert|58252.0|31.0|https://www.openml.org/d/41147|https://www.openml.org/d/45035| |
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|compas-two-years|4966.0|11.0|https://www.openml.org/d/42192|https://www.openml.org/d/45039| |
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|default-of-credit-card-clients|13272.0|21.0|https://www.openml.org/d/42477|https://www.openml.org/d/45036| |
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|road-safety|111762.0|32.0|https://www.openml.org/d/42803|https://www.openml.org/d/45038| |
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**Numerical Regression** |
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|dataset_name|n_samples|n_features|original_link|new_link| |
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|cpu_act|8192.0|21.0|https://www.openml.org/d/197|https://www.openml.org/d/44132| |
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|pol|15000.0|26.0|https://www.openml.org/d/201|https://www.openml.org/d/44133| |
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|elevators|16599.0|16.0|https://www.openml.org/d/216|https://www.openml.org/d/44134| |
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|wine_quality|6497.0|11.0|https://www.openml.org/d/287|https://www.openml.org/d/44136| |
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|Ailerons|13750.0|33.0|https://www.openml.org/d/296|https://www.openml.org/d/44137| |
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|yprop_4_1|8885.0|42.0|https://www.openml.org/d/416|https://www.openml.org/d/45032| |
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|houses|20640.0|8.0|https://www.openml.org/d/537|https://www.openml.org/d/44138| |
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|house_16H|22784.0|16.0|https://www.openml.org/d/574|https://www.openml.org/d/44139| |
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|delays_zurich_transport|5465575.0|9.0|https://www.openml.org/d/40753|https://www.openml.org/d/45034| |
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|diamonds|53940.0|6.0|https://www.openml.org/d/42225|https://www.openml.org/d/44140| |
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|Brazilian_houses|10692.0|8.0|https://www.openml.org/d/42688|https://www.openml.org/d/44141| |
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|Bike_Sharing_Demand|17379.0|6.0|https://www.openml.org/d/42712|https://www.openml.org/d/44142| |
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|nyc-taxi-green-dec-2016|581835.0|9.0|https://www.openml.org/d/42729|https://www.openml.org/d/44143| |
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|house_sales|21613.0|15.0|https://www.openml.org/d/42731|https://www.openml.org/d/44144| |
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|sulfur|10081.0|6.0|https://www.openml.org/d/23515|https://www.openml.org/d/44145| |
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|medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/44146| |
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|MiamiHousing2016|13932.0|14.0|https://www.openml.org/d/43093|https://www.openml.org/d/44147| |
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|superconduct|21263.0|79.0|https://www.openml.org/d/43174|https://www.openml.org/d/44148| |
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**Categorical Regression** |
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|dataset_name|n_samples|n_features|original_link|new_link| |
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|topo_2_1|8885.0|255.0|https://www.openml.org/d/422|https://www.openml.org/d/45041| |
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|analcatdata_supreme|4052.0|7.0|https://www.openml.org/d/504|https://www.openml.org/d/44055| |
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|visualizing_soil|8641.0|4.0|https://www.openml.org/d/688|https://www.openml.org/d/44056| |
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|delays_zurich_transport|5465575.0|12.0|https://www.openml.org/d/40753|https://www.openml.org/d/45045| |
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|diamonds|53940.0|9.0|https://www.openml.org/d/42225|https://www.openml.org/d/44059| |
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|Allstate_Claims_Severity|188318.0|124.0|https://www.openml.org/d/42571|https://www.openml.org/d/45046| |
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|Mercedes_Benz_Greener_Manufacturing|4209.0|359.0|https://www.openml.org/d/42570|https://www.openml.org/d/44061| |
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|Brazilian_houses|10692.0|11.0|https://www.openml.org/d/42688|https://www.openml.org/d/44062| |
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|Bike_Sharing_Demand|17379.0|11.0|https://www.openml.org/d/42712|https://www.openml.org/d/44063| |
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|Airlines_DepDelay_1M|1000000.0|5.0|https://www.openml.org/d/42721|https://www.openml.org/d/45047| |
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|nyc-taxi-green-dec-2016|581835.0|16.0|https://www.openml.org/d/42729|https://www.openml.org/d/44065| |
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|abalone|4177.0|8.0|https://www.openml.org/d/42726|https://www.openml.org/d/45042| |
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|house_sales|21613.0|17.0|https://www.openml.org/d/42731|https://www.openml.org/d/44066| |
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|seattlecrime6|52031.0|4.0|https://www.openml.org/d/42496|https://www.openml.org/d/45043| |
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|medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/45048| |
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|particulate-matter-ukair-2017|394299.0|6.0|https://www.openml.org/d/42207|https://www.openml.org/d/44068| |
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|SGEMM_GPU_kernel_performance|241600.0|9.0|https://www.openml.org/d/43144|https://www.openml.org/d/44069| |
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### Dataset Curators |
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Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. |
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### Licensing Information |
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[More Information Needed] |
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### Citation Information |
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Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. Why do tree-based models still outperform deep |
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learning on typical tabular data?. NeurIPS 2022 Datasets and Benchmarks Track, Nov 2022, New |
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Orleans, United States. ffhal-03723551v2f |
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