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">MageBench Leaderboard</h1>"""

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

MageBench is a reasoning-oriented multimodal intelligent agent benchmark introduced in the paper ["xxx"](https://www.arxiv.com). 
The tasks we selected meet the following criteria: 
- Simple environment, 
- Reflect a certain reasoning ability，
- High level of visual involvement.

In our paper, we demonstrate that our benchmark can generalize well to other scenarios. 
We hope our work can empower future research in the fields of intelligent agents, robotics, and more.
"""

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

This platform will not run your model for testing, it only provides a leaderboard. 
You need to choose a preset that matches your results, test it in your local environment, 
and then submit the results to us for approval. Once approved, we will make your results public.

## Reproducibility

Since we are unable to reproduce the submitter's results, to ensure the reliability of the results, 
we require all submitters to provide either a link to a paper/blog/report that includes contact information or an open-source GitHub link that reproduces the results.
**Results that do not meet the above conditions or have other issues affecting fairness 
(such as incorrect setting category) will be removed by us.**

"""

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"""
"""