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">Eval-Anything Leaderboard</h1>"""

# MJB_LOGO = '<img src="" alt="Logo" style="width: 100%; display: block; margin: auto;">'

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
Eval-anything is a framework designed specifically for evaluating all-modality models, and it is a part of the [Align-Anything](https://github.com/PKU-Alignment/align-anything) framework. It consists of two main tasks: All-Modality Understanding (AMU) and All-Modality Generation (AMG). AMU assesses a model's ability to simultaneously process and integrate information from all modalities, including text, images, audio, and video. On the other hand, AMG evaluates a model's capability to autonomously select output modalities based on user instructions and synergistically utilize different modalities to generate output. Eval-anything aims to comprehensively assess the ability of all-modality models to handle heterogeneous data from multiple sources, providing a reliable evaluation tool for this field.

**Note:** Since most current open-source models lack support for all-modality output, (†) indicates that models are used as agents to invoke [AudioLDM2-Large](https://huggingface.co/cvssp/audioldm2-large) and [FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell) for audio and image generation.
"""

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

EVALUATION_QUEUE_TEXT = """
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = """
@misc{align_anything,
  author = {PKU-Alignment Team},
  title = {Align Anything: training all modality models to follow instructions with unified language feedback},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\\url{https://github.com/PKU-Alignment/align-anything}},
}
"""


ABOUT_TEXT = """
We will provide methods to upload more model evaluation results in the future.
"""