website_texts.py

from __future__ import annotations

TITLE = """<h1 align="center" id="space-title">TabArena Leaderboard for Predictive Machine Learning on IID Tabular Data</h1>"""
INTRODUCTION_TEXT = """
TabArena is a living benchmark system for predictive machine learning on tabular data.
The goal of TabArena and its leaderboard is to asses the peak performance of
model-specific pipelines.
"""

OVERVIEW_DATASETS = """
The leaderboard is based on a manually curated collection of
51 tabular classification and regression datasets for independent and identically distributed
(IID) data, spanning the small to medium data regime. The datasets were carefully
curated to represent various real-world predictive machine learning use cases.
"""
OVERVIEW_MODELS = """
The focus of the leaderboard is on model-specific pipelines. Each pipeline
is evaluated with default and tuned hyperparameter configuration or as an ensemble of
tuned configurations. Each model is implemented in a tested real-world pipeline that was
optimized to get the most out of the model by the maintainers of TabArena, and where
possible together with the authors of the model.
"""
OVERVIEW_METRICS = """
The leaderboards are ranked based on Elo. We present several additional
metrics. See `More Details` for more information on the metrics.

**Note, we impute** the performance for models that cannot run on all datasets due to
task or dataset size constraints (e.g. TabPFN, TabICL). In general, imputation
negatively represents the model performance, punishing the model for not being able
to run on all datasets. We provide leaderboards computed only on the subset of datasets
where TabPFN, TabICL, or both can run. We denote these leaderboards by adding a
`X-compatible` postfix.
"""
OVERVIEW_REF_PIPE = """
The leaderboard includes a reference pipeline, which is applied
independently of the tuning protocol and constraints we constructed for models within TabArena.
The reference pipeline aims to represent the performance quickly achievable by a
practitioner on a dataset. The current reference pipeline is the predictive machine
learning system AutoGluon (version 1.3, with the best_quality preset and
4 hours for training). AutoGluon represents an ensemble pipeline across various model
types and thus provides a reference for model-specific pipelines.
"""

ABOUT_TEXT = r"""
### Extended Overview of TabArena (References / Papers)
We introduce TabArena and provide an overview of TabArena-v0.1 in our paper: TBA.

### Using TabArena for Benchmarking
To compare your own methods to the pre-computed results for all models on the leaderboard,
you can use the TabArena framework. For examples on how to use TabArena for benchmarking,
please see https://tabarena.ai/code-examples

### Contributing to the Leaderboard; Contributing Models
For guidelines on how to contribute your model to TabArena, or the result of your model
to the official leaderboard, please see the appendix of our paper: TBA.

### Contributing Data
For anything related to the datasets used in TabArena, please see https://tabarena.ai/data-tabular-ml-iid-study

---

### Leaderboard Documentation

The leaderboard is ranked by Elo and includes several other metrics. Here is a short
description for these metrics:

#### Elo
We evaluate models using the Elo rating system, following Chatbot Arena. Elo is a
pairwise comparison-based rating system where each model's rating predicts its expected
win probability against others, with a 400-point Elo gap corresponding to a 10 to 1
(91\%) expected win rate. We calibrate 1000 Elo to the performance of our default
random forest configuration across all figures, and perform 100 rounds of bootstrapping
to obtain 95\% confidence intervals. Elo scores are computed using ROC AUC for binary
classification, log-loss for multiclass classification, and RMSE for regression.

#### Normalized Score
Following TabRepo, we linearly rescale the error such that the best method has a
normalized score of one, and the median method has a normalized score of 0. Scores
below zero are clipped to zero. These scores are then averaged across datasets.

#### Average Rank
Ranks of methods are computed on each dataset (lower is better) and averaged.

#### Harmonic Mean Rank
Taking the harmonic mean of ranks, 1/((1/N) * sum(1/rank_i for i in range(N))),
more strongly favors methods having very low ranks on some datasets. It therefore favors
 methods that are sometimes very good and sometimes very bad over methods that are
 always mediocre, as the former are more likely to be useful in conjunction with
 other methods.

#### Improvability
We introduce improvability as a metric that measures how many percent lower the error
of the best method is than the current method on a dataset. This is then averaged over
datasets. Formally, for a single dataset improvability is (err_i - besterr_i)/err_i * 100\%.
Improvability is always between 0\% and 100\%.

---

### Contact

For most inquires, please open issues in the relevant GitHub repository or here on
HuggingFace.

For any other inquiries related to TabArena, please reach out to: mail@tabarena.ai

### Core Maintainers
The current core maintainers of TabArena are:
[Nick Erickson](https://github.com/Innixma),
[Lennart Purucker](https://github.com/LennartPurucker/),
[Andrej Tschalzev](https://github.com/atschalz),
[David Holzmüller](https://github.com/dholzmueller)
"""

CITATION_BUTTON_LABEL = (
    "If you use TabArena or the leaderboard in your research please cite the following:"
)
CITATION_BUTTON_TEXT = r"""@article{
    TBA,
}
"""

VERSION_HISTORY_BUTTON_TEXT = """
**Current Version: TabArena-v0.1.1**

The following details updates to the leaderboard (date format is YYYY/MM/DD):

* 2025/06/13: Add data for all subsets and re-runs on GPU; Add leaderboards for subsets;
 new overview; add Figures to LBs.
* 2025/05: Initialization of the TabArena-v0.1 leaderboard.
"""