src/about.py

from dataclasses import dataclass
from enum import Enum

@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str
    reference_url: 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("aiera_transcript_sentiment", "accuracy,none","Sentiment", reference_url="https://huggingface.co/datasets/Aiera/aiera-transcript-sentiment")
    task1 = Task("aiera_ect_sum", "rougeLsum,none","Summary", reference_url="https://huggingface.co/datasets/Aiera/aiera-ect-sum")
    task2 = Task("finqa", "exact_match_manual,none","Q&A", reference_url="https://huggingface.co/datasets/Aiera/finqa-verified")
    task3 = Task("aiera_speaker_assign", "accuracy,none", "Speaker ID", reference_url="https://huggingface.co/datasets/Aiera/aiera-speaker-assign")

    #task7 = Task("flare_ner", "accuracy,none","flare-ner", reference_url="test")


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


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">Aiera Leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
The Aiera Leaderboard evaluates the performance of LLMs on a number of financial intelligence tasks including:
* Assignments of speakers for event transcript segments and identification of speaker changes. 
* Abstractive summarizations of earnings call transcripts.
* Calculation-based Q&A over financial text. 
* Financial sentiment tagging for transcript segments.

A guide for eval tasks is avaliable on github at [https://github.com/aiera-inc/aiera-benchmark-tasks](https://github.com/aiera-inc/aiera-benchmark-tasks).
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## How it works
Models are evaluated on the following tasks
* **aiera_speaker_assign**: Assignments of speakers for event transcript segments and identification of speaker changes. Dataset available on [huggingface](https://huggingface.co/datasets/Aiera/aiera-speaker-assign).
* **aiera-ect-sum**: Abstractive summarizations of earnings call transcripts. Dataset available on [huggingface](https://huggingface.co/datasets/Aiera/aiera-ect-sum).
* **finqa**: Calculation-based Q&A over financial text. Dataset available on [huggingface](https://huggingface.co/datasets/Aiera/finqa-verified).
* **aiera-transcript-sentiment**: Event transcript segments with labels indicating the financial sentiment. Dataset available on [huggingface](https://huggingface.co/datasets/Aiera/aiera-transcript-sentiment).

## Reproducibility
A guide for running the above tasks using EleutherAi's lm-evaluation-harness is avaliable on github at [https://github.com/aiera-inc/aiera-benchmark-tasks](https://github.com/aiera-inc/aiera-benchmark-tasks).

"""

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). A guide for running the Aiera's tasks using EleutherAi's lm-evaluation-harness is avaliable on github at [https://github.com/aiera-inc/aiera-benchmark-tasks](https://github.com/aiera-inc/aiera-benchmark-tasks).

"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""@misc{aiera-finance-leaderboard,
  author = {Jacqueline Garrahan, Bryan Healey},
  title = {Aiera Finance Leaderboard},
  year = {2024},
  publisher = {Aiera},
  howpublished = "\url{https://huggingface.co/spaces/Aiera/aiera-finance-leaderboard}"
}
@software{eval-harness,
  author       = {Gao, Leo and
                  Tow, Jonathan and
                  Biderman, Stella and
                  Black, Sid and
                  DiPofi, Anthony and
                  Foster, Charles and
                  Golding, Laurence and
                  Hsu, Jeffrey and
                  McDonell, Kyle and
                  Muennighoff, Niklas and
                  Phang, Jason and
                  Reynolds, Laria and
                  Tang, Eric and
                  Thite, Anish and
                  Wang, Ben and
                  Wang, Kevin and
                  Zou, Andy},
  title        = {A framework for few-shot language model evaluation},
  month        = sep,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.0.1},
  doi          = {10.5281/zenodo.5371628},
  url          = {https://doi.org/10.5281/zenodo.5371628}
}
"""