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("Appendicitis", "acc", "Appendicits")
    task1 = Task("Cholecystitis", "acc", "Cholecystitis")
    task2 = Task("Diverticulitis", "acc", "Diverticulitis")
    task3 = Task("Pancreatitis", "acc", "Pancreatitis")


NUM_FEWSHOT = 0  # Change with your few shot
# ---------------------------------------------------


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">MIMIC Clinical Decision Making</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
This leaderboard shows current scores of models on the MIMIC Clinical Decision Making (MIMIC-CDM) and MIMIC Clinical Decision Making Full Information (MIMIC-CDM-FI) datasets. The dataset can be found [here](https://physionet.org/content/mimic-iv-ext-cdm/). The code used to run the models can be found [here](https://github.com/paulhager/MIMIC-Clinical-Decision-Making-Framework).
"""

# 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:

For MIMIC-CDM, navigate to the MIMIC-Clinical-Decision-Making-Framework repository and execute:

```
python run.py pathology=appendicitis model=<YOUR_MODEL_NAME>
python run.py pathology=cholecystitis model=<YOUR_MODEL_NAME>
python run.py pathology=pancreatitis model=<YOUR_MODEL_NAME>
python run.py pathology=diverticulitis model=<YOUR_MODEL_NAME>
```

For MIMIC-CDM-FI, navigate to the MIMIC-Clinical-Decision-Making-Framework repository and execute:

```
python run_full_info.py pathology=appendicitis model=<YOUR_MODEL_NAME>
python run_full_info.py pathology=cholecystitis model=<YOUR_MODEL_NAME>
python run_full_info.py pathology=pancreatitis model=<YOUR_MODEL_NAME>
python run_full_info.py pathology=diverticulitis model=<YOUR_MODEL_NAME>

```

"""

# 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"""
"""