Delete about.py

about.py

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-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 
-    # For MMLongBench-Doc (https://arxiv.org/abs/2407.01523), we use ACC as the main metric
-    task0 = Task("mmlongbench_doc", "acc", "ACC")
-
-NUM_FEWSHOT = 0 # Change with your few shot
-# ---------------------------------------------------
-
-
-
-# Your leaderboard name
-TITLE = """<h1 align="center" id="space-title">🥇 <a href="" target="_blank">UniGenBench</a> Leaderboard</h1>"""
-
-# Links and conference info
-LINKS_AND_INFO = """
-<div align="center">
-<p><a href="https://github.com/CodeGoat24/UnifiedReward" target="_blank">UnifiedReward Team</a></p>
-<p>
-<a href="" target="_blank">🏠 Homepage</a> | 
-<a href="" target="_blank">📄 arXiv Paper</a> | 
-</p>
-</div>
-"""
-
-# What does your leaderboard evaluate?
-INTRODUCTION_TEXT = """
-📚 [UniGenBench]() is a unified and versatile benchmark for T2I generation that integrates diverse prompt themes with a comprehensive suite of fine-grained evaluation criteria. 
-
-🔧 You can use the official [GitHub repo](https://github.com/CodeGoat24/UniGenBench) to evaluate your model on [UniGenBench]().
-
-📝 To add your own model to the leaderboard, please send an Email to yibinwang1121@163.com, then we will help with the evaluation and updating the leaderboard.
-"""
-
-# Which evaluations are you running? how can people reproduce what you have?
-LLM_BENCHMARKS_TEXT = f"""
-## How it works
-
-[MMLongBench-Doc](https://arxiv.org/abs/2407.01523) evaluates multimodal models on their ability to understand long documents containing both text and visual elements. The benchmark includes various document understanding tasks that require models to process and reason over extended contexts.
-
-## Evaluation Metrics
-
-- **ACC (Accuracy)**: The primary metric measuring the overall accuracy of model predictions on document understanding tasks.
-- **Parameters**: Model size in billions of parameters
-- **Open Source**: Whether the model weights are publicly available
-
-## Reproducibility
-
-To reproduce our results, please refer to the official [MMLongBench-Doc](https://arxiv.org/abs/2407.01523) repository for evaluation scripts and detailed instructions.
-"""
-
-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"""
-
-}"""