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
# ---------------------------------------------------



TITLE = """<h1 align="center" id="space-title">LLM Hallucination Detection Leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
<!--
keywords: LLM hallucination detection, hallucination leaderboard, RAG hallucination benchmark, UltraChat hallucination rate, Verify API, kluster.ai, factual accuracy of language models, large language model evaluation
-->

The **LLM Hallucination Detection Leaderboard** is a public, continuously updated comparison of how well popular Large Language Models (LLMs) avoid *hallucinations*, responses that are factually incorrect, fabricated, or unsupported by evidence. By surfacing transparent metrics across tasks, we help practitioners choose models that they can trust in production.

### Why does hallucination detection matter?

* **User Trust & Safety** – Hallucinations undermine confidence and can damage reputation.
* **Retrieval-Augmented Generation (RAG) Quality** – In enterprise workflows, LLMs must remain faithful to supplied context. Measuring hallucinations highlights which models respect that constraint.
* **Regulatory & Compliance Pressure** – Upcoming AI regulations require demonstrable accuracy standards. Reliable hallucination metrics can help you meet these requirements.

### How we measure hallucinations

We evaluate each model on two complementary benchmarks and compute a *hallucination rate* (lower = better):

1. **HaluEval-QA (RAG setting)** – Given a question *and* a supporting document, the model must answer *only* using the provided context.
2. **UltraChat Filtered (Non-RAG setting)** – Open-domain questions with **no** extra context test the model's internal knowledge.

Outputs are automatically verified by [Verify](https://platform.kluster.ai/verify) from [kluster.ai](https://kluster.ai/), which cross-checks claims against the source document or web results.

---

Stay informed as we add new models and tasks, and follow us on [X](https://x.com/klusterai) or join Discord [here](https://discord.com/invite/klusterai) for the latest updates on trustworthy LLMs.
"""

LLM_BENCHMARKS_TEXT = f"""
## How it works

## Reproducibility
To reproduce our results, here is the commands you can run:

"""

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"""
# """