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 English, Chinese and SEA languages, focusing on world knowledge and reasoning abilities. The five languages for evaluation are English (en), Chinese (zh), Indonesian (id), Thai (th), and Vietnamese (vi).

## Datasets
The benchmark data can be found in the [SeaExam dataset](https://huggingface.co/datasets/SeaLLMs/SeaExam). The dataset consists of two tasks:
- [**M3Exam**](https://arxiv.org/abs/2306.05179): a benchmark sourced from real and official human exam questions for evaluating LLMs in a multilingual, multimodal, and multilevel context. We post-process the data for the 5 languages. 
- [**MMLU**](https://arxiv.org/abs/2009.03300): a test to measure a text model's multitask accuracy in English. The test covers 57 tasks. We sample 50 questions from each task and translate the data into the other 4 languages with google translate.

## Evalation Criteria
We evaluate the models with accuracy score. The leaderboard is sorted by the average score across SEA languages (id, th, and vi).

We have the following settings for evaluation:
- **few-shot**: the default setting is few-shot (3-shot). All open-source models are evaluated with 3-shot.
- **zero-shot**: the zero-shot setting is also available. As closed-source models has format issues with few-shot, they are evaluated with zero-shot.


## Reults
You can find the detailed numerical results in the results Hugging Face dataset: https://huggingface.co/datasets/SeaLLMs/SeaExam-results

## Reproducibility
To reproduce our results, use the script in [this repo](https://github.com/DAMO-NLP-SG/SeaExam/tree/main). The script will download the model and tokenizer, and evaluate the model on the benchmark data.
```python
python scripts/main.py --model $model_name_or_path
```

"""

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"""
"""

CONTACT_TEXT = f"""
## Contact

If you have any questions or want to include your models in the leaderboard, please contact Chaoqun Liu (<chaoqun.liu@alibaba-inc.com>) and Wenxuan Zhang (<saike.zwx@alibaba-inc.com>).
"""