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("en_prompts", "f1", "PromptsEN")
    task1 = Task("en_responses", "f1", "ResponsesEN")
    task2 = Task("de_prompts", "f1", "PromptsDE")
    task3 = Task("fr_prompts", "f1", "PromptsFR")
    task4 = Task("it_prompts", "f1", "PromptsIT")
    task5 = Task("es_prompts", "f1", "PromptsES")

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



# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">GuardBench · A Leaderboard for Guardrail Models</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """"""

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

Welcome to the 🌟 GuardBench Leaderboard 🚀, an independent benchmark designed to evaluate guardrail models.  
Evaluation results are shown in terms of F1.  
For fine-grained evaluation, please see our publications referenced below.

## Guardrail Models
Guardrail models are Large Language Models fine-tuned for safety classification and employed to detect unsafe content in human-AI interactions.  
By complementing other safety measures such as safety alignment, they aim to prevent generative AI systems from providing harmful information to the users.

## GuardBench
GuardBench is a large-scale benchmark for guardrail models comprising 40 safety evaluation datasets that was recently proposed to evaluate their effectiveness.  
You can find more information in the [paper]((https://aclanthology.org/2024.emnlp-main.1022/)) we presented at EMNLP 2024.

## Python
GuardBench is accompained by a [Python library](https://github.com/AmenRa/GuardBench) providing evaluation functionalities on top of it.

## Evaluation Metric
Evaluation results are shown in terms of F1.  
We do not employ the Area Under the Precision-Recall Curve (AUPRC) as we found it overemphasizes models' Precision at the expense of Recall, thus hiding significant performance details.  
We rely on [Scikit-Learn](https://scikit-learn.org/stable) to compute metric scores.

## Reproducibility
Coming soon.
"""

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"""@inproceedings{guardbench,
    title = "{G}uard{B}ench: A Large-Scale Benchmark for Guardrail Models",
    author = "Bassani, Elias  and
      Sanchez, Ignacio",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.1022",
    doi = "10.18653/v1/2024.emnlp-main.1022",
    pages = "18393--18409",
}"""

CITATION_TEXT = """Copy the following snippet to cite these results.

```bibtex
@inproceedings{guardbench,
    title = "{G}uard{B}ench: A Large-Scale Benchmark for Guardrail Models",
    author = "Bassani, Elias  and
      Sanchez, Ignacio",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.1022",
    doi = "10.18653/v1/2024.emnlp-main.1022",
    pages = "18393--18409",
}
```
"""