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from dataclasses import dataclass | |
from enum import Enum | |
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">TempCompass leaderboard</h1>""" | |
# What does your leaderboard evaluate? | |
INTRODUCTION_TEXT = """ | |
Welcome to the leaderboard of TempCompass! π | |
TempCompass is a benchmark to evaluate the temporal perception ability of Video LLMs. It consists of 410 videos and 7,540 task instructions, covering 11 temporal aspects and 4 task types. Please refer to [our paper](https://arxiv.org/abs/2403.00476) for more details. | |
""" | |
# Which evaluations are you running? how can people reproduce what you have? | |
LLM_BENCHMARKS_TEXT = f""" | |
## How it works | |
## 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 π€ | |
### 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""" | |
@article{liu2024tempcompass, | |
title = {TempCompass: Do Video LLMs Really Understand Videos?}, | |
author = {Yuanxin Liu and Shicheng Li and Yi Liu and Yuxiang Wang and Shuhuai Ren and Lei Li and Sishuo Chen and Xu Sun and Lu Hou}, | |
year = {2024}, | |
journal = {arXiv preprint arXiv: 2403.00476} | |
} | |
""" | |