src/display/about.py

from dataclasses import dataclass
from enum import Enum

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


# Init: to update with your specific keys
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    task0 = Task("task_name1", "metric_name", "First task")
    task1 = Task("task_name2", "metric_name", "Second task")


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">📃 SeaExam Leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
This leaderboard is specifically designed to evaluate large language models (LLMs) for Southeast Asian (SEA) languages. It assesses model performance using human-exam type benchmarks, reflecting the model's world knowledge (e.g., with language or social science subjects) and reasoning abilities (e.g., with mathematics or natural science subjects).

For additional details such as datasets, evaluation criteria, and reproducibility, please refer to the "📝 About" tab.

Also check the [SeaBench leaderboard](https://huggingface.co/spaces/SeaLLMs/SeaBench_leaderboard) - focusing on evaluating the model's ability to respond to general human instructions in real-world multi-turn settings.

"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
# About
Even though large language models (LLMs) have shown impressive performance on various benchmarks for English, their performance on Southeast Asian (SEA) languages is still underexplored. This leaderboard aims to evaluate LLMs on exam-type benchmarks for SEA languages, focusing on world knowledge and reasoning abilities.

## Datasets
The leaderboard evaluates models on the following tasks:
- **M3Exam**: 
- **MMLU**:

## Evalation Criteria

## Reults

## 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"""
"""