src/about.py

from dataclasses import dataclass
from enum import Enum


@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard
    task0 = Task("anli_r1", "acc", "ANLI")
    task1 = Task("logiqa", "acc_norm", "LogiQA")


NUM_FEWSHOT = 0  # Change with your few shot
# ---------------------------------------------------


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">BOOM 💥 Observability Time-Series Forecasting Leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
BOOM (**B**enchmark **o**f **O**bservability **M**etrics) is a large-scale, real-world time series dataset designed for evaluating models on forecasting tasks in complex observability environments. Consisting of around 350 million time-series data points spanning 32,887 variables, the benchmark is derived from real-world metrics collected via Datadog, a leading observability platform. It therefore captures the irregularity, structural complexity, and heavy-tailed statistics typical of production observability data. 
For more information, please refer to the [BOOM Dataset Card](https://huggingface.co/datasets/Datadog/BOOM) and the [BOOM GitHub repository](https://github.com/DataDog/toto?tab=readme-ov-file#boom-benchmark-of-observability-metrics)
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""

BOOM is a large-scale, real-world time series dataset designed for benchmarking forecasting models in observability environments. The dataset captures the complexity and irregularity of production observability data, making it a challenging and realistic testbed for time series forecasting. BOOM consists of approximately 350 million time-series points across 32,887 variates. The dataset is split into 2,807 individual time series with one or multiple variates.
For more details and dataset structure, please refer to the [BOOM Dataset Card](https://huggingface.co/datasets/Datadog/BOOM).

The evaluation procedure is inspired by [Gift-Eval](https://github.com/SalesforceAIResearch/gift-eval): We evaluate models using **MASE (Mean Absolute Scaled Error)** for forecast accuracy, **CRPS (Continuous Ranked Probability Score)** for probabilistic forecast quality, and **Rank**—which determines overall performance and is used to order models on the leaderboard.

To reproduce our results, we provide a guide in the [BOOM GitHub repository](https://github.com/DataDog/toto/tree/main/boom) that explains how to install the required dependencies and includes example notebooks demonstrating how to evaluate both foundation models and statistical baselines on BOOM.

"""

EVALUATION_QUEUE_TEXT = """
## Some good practices before submitting a model

### 1) Make sure you can load your model and tokenizer using AutoClasses:
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModel.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
```
If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.

Note: make sure your model is public!
Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!

### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!

### 3) Make sure your model has an open license!
This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗

### 4) Fill up your model card
When we add extra information about models to the leaderboard, it will be automatically taken from the model card

## In case of model failure
If your model is displayed in the `FAILED` category, its execution stopped.
Make sure you have followed the above steps first.
If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@misc{toto2025,
  title={This Time is Different: An Observability Perspective on Time Series Foundation Models},
  author={TODO},
  year={2025},
  eprint={arXiv:TODO},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}
"""