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from dataclasses import dataclass
from enum import Enum
@dataclass
class Task:
phenotype: str
metric: str
# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
task0 = Task("Asthma", "auroc")
task1 = Task("Cataract", "auroc")
task2 = Task("Diabetes", "auroc")
task3 = Task("GERD", "auroc")
task4 = Task("Hay-fever & Eczema", "auroc")
task5 = Task("Major depression", "auroc")
task6 = Task("Myocardial infarction", "auroc")
task7 = Task("Osteoarthritis", "auroc")
task8 = Task("Pneumonia", "auroc")
task9 = Task("Stroke", "auroc")
task10 = Task("Asthma", "auprc")
task11 = Task("Cataract", "auprc")
task12 = Task("Diabetes", "auprc")
task13 = Task("GERD", "auprc")
task14 = Task("Hay-fever & Eczema", "auprc")
task15 = Task("Major depression", "auprc")
task16 = Task("Myocardial infarction", "auprc")
task17 = Task("Osteoarthritis", "auprc")
task18 = Task("Pneumonia", "auprc")
task19 = Task("Stroke", "auprc")
# ---------------------------------------------------
# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">LLMs Disease Risk Prediction Leaderboard</h1>"""
# What does your leaderboard evaluate?
# INTRODUCTION_TEXT = """
# TODO:
# - Add a description of the leaderboard
# - Add class distribution for each phenotype
# - Potentially a warning when we should not rely on AUROC
# - Plot of AUROC and AUPRC for each phenotype
# - Edit about section
# - Edit submit section (AutoModelForCausalLM)
# """
INTRODUCTION_TEXT = """
"""
# 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, AutoModelForCausalLM, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModelForCausalLM.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!
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, feel free to open a new discussion on the Hugging Face space.
"""
CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@misc{
TemryL/LLM-Disease-Risk-Leaderboard,
author = {Tom Mery, Chirag Patel},
title = {TemryL/LLM-Disease-Risk-Leaderboard},
year = {2024},
publisher = {Hugging Face},
howpublished = "\url{https://huggingface.co/spaces/TemryL/LLM-Disease-Risk-Leaderboard}"
}
"""
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