src/display/about.py

from dataclasses import dataclass
from enum import Enum

@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Init: to update with your specific keys
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard
    # task0 = Task("task_name1", "metric_name", "First task")
    # task1 = Task("task_name2", "metric_name", "Second task")

    task0 = Task("finance_bench", "accuracy", "FinanceBench")
    task1 = Task("legal_confidentiality", "exact_match", "Legal Confidentiality")
    task2 = Task("writing_prompts", "engagingness", "Writing Prompts")
    task3 = Task("customer_support_dialogue", "relevance", "Customer Support Dialogue")
    task4 = Task("toxic_prompts", "toxicity", "Toxic Prompts")
    task5 = Task("enterprise_pii", "enterprise_pii", "Enterprise PII")


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

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
Enterprise Scenarios leaderboard evaluates the performance of language models on real-world enterprise use cases. We provide 6 benchmarks that cover diverse tasks. Some of our test sets are closed source. The primary motivation behind this is to prevent gaming of the leaderboard by fine-tuning models on our test sets. Validation sets for each of the tasks can be found in https://huggingface.co/PatronusAI.
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## How it works

## Tasks
1.FinanceBench (Islam, Pranab, et al. "FinanceBench: A New Benchmark for Financial Question Answering."): The task measures the ability to answer financial questions given the retrieved context from a document and a question. We do not evaluate the retrieval capabilities for this task. We only evaluate the accuracy of the answers.The dataset can be 
found at https://huggingface.co/datasets/PatronusAI/financebench. 

2.Legal Confidentiality: We use a subset of 100 labeled prompts from LegalBench (Guha, et al. LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in \
Large Language Models) to measure the ability of LLMs to reason over legal causes. The model is prompted to return yes/no as an answer to the question. 

3.Writing Prompts: This task evaluates the story-writing and creative abilities of the LLM. We measure the engagingness of the text generated by the LLM.  The dataset is a mix of human annotated samples from r/WritingPrompts and redteaming generations.

4.Customer Support Dialogue: This task evaluates the ability of the LLM to answer a customer support question given some product information and conversational history. We measure the relevance of the generation given the conversational history, product information and question by the customer.

5.Toxic Prompts: This task evaluates the safety of the model by using prompts that can elicit harmful information from LLMs. We measure if the model generates toxic content.

6.Enterprise PII: This task evaluates the business safety of the model by using prompts to elicit business-sensitive information from LLMs. We measure if the model generates business sensitive information. 

## What is Patronus AI?

Patronus AI provides an automated evaluation platform for LLMs. Our platform allows companies to manage evaluation runs, monitor LLMs in production and find edge cases where models will fail. We provide auto-generation of adversarial test sets along with Patronus-managed datasets to find failure cases. 

To learn more about us, visit: https://www.patronus.ai/  

To contact us, please reach out at contact@patronus.ai.


"""


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, open a new discussion in the community section of the leaderboard and we will look into it.
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
cff-version: 1.2.0
title: Enterprise Scenarios leaderboard
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Selvan Sunitha
    family-names: Ravi
    email: sunitha@patronus.ai
    affiliation: Patronus AI
version: 1.0.0
"""