src/about.py

from dataclasses import dataclass
from enum import Enum

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

    # base
    task0 = Task("arc_easy", "accuracy", "ARC-Easy", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/arc")
    task1 = Task("arc_challenge", "accuracy", "ARC-Challenge", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/arc")
    task2 = Task("drop", "mean", "DROP", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/drop")
    task3 = Task("winogrande", "accuracy", "WinoGrande", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/winogrande")
    task4 = Task("gsm8k", "accuracy", "GSM8K", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/gsm8k")
    task5 = Task("hellaswag", "accuracy", "HellaSwag", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/hellaswag")
    task6 = Task("humaneval", "mean", "HumanEval", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/humaneval")
    task7 = Task("ifeval", "final_acc", "IFEval", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/ifeval")
    task8 = Task("math", "accuracy", "MATH", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/mathematics")
    task9 = Task("mmlu", "accuracy", "MMLU", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/mmlu")
    task10 = Task("mmlu_pro", "accuracy", "MMLU-Pro", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/mmlu_pro")
    task11 = Task("gpqa_diamond", "accuracy", "GPQA-Diamond", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/gpqa")
    task12 = Task("mmmu_multiple_choice", "accuracy", "MMMU-Multiple-Choice", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/mmmu")
    task13 = Task("mmmu_open", "accuracy", "MMMU-Open-Ended", "base", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/mmmu")

    # agentic
    task14 = Task("gaia", "mean", "GAIA", "agentic", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/gaia")
    task15 = Task("gdm_intercode_ctf", "accuracy", "GDM-InterCode-CTF", "agentic", "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/gdm_capabilities/intercode_ctf")


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



# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">Vector State of Evaluation Leaderboard</h1>"""

SINGLE_TURN_TASK_NAMES = ", ".join([f"[{task.value.col_name}]({task.value.source})" for task in Tasks if task.value.type == "base"])
AGENTIC_TASK_NAMES = ", ".join([f"[{task.value.col_name}]({task.value.source})" for task in Tasks if task.value.type == "agentic"])

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = f"""
This leaderboard presents the performance of selected LLM models on a set of tasks. The tasks are divided into two categories: base and agentic. The base tasks are: {SINGLE_TURN_TASK_NAMES}. The agentic tasks are: {AGENTIC_TASK_NAMES}."""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
# Vector State of Evaluation Leaderboard

## Overview
The **Vector State of Evaluation Leaderboard** presents the performance of selected LLM models on a variety of tasks. These tasks are divided into two categories:

- **Base Tasks**: ARC-Easy, ARC-Challenge, DROP, WinoGrande, GSM8K, HellaSwag, HumanEval, IFEval, MATH, MMLU, MMLU-Pro, GPQA-Diamond, MMMU-Multiple-Choice, MMMU-Open-Ended  
- **Agentic Tasks**: GAIA, GDM-InterCode-CTF  

Users can compare models side by side to see how they perform on both base-level understanding tasks and more advanced, “agentic” tasks.

## Vector Institute
The **Vector Institute** is dedicated to advancing the fields of artificial intelligence and machine learning through cutting-edge research, collaborative projects, and open-source contributions. This leaderboard is part of Vector’s broader effort to promote transparency and progress in AI research.

## Model
We evaluate a variety of **Large Language Models (LLMs)** across the included benchmarks. Each model:
- Is tested on the same set of tasks.
- Has standardized prompts or evaluation methodologies.
- Generates performance metrics (accuracy, F1, etc.) for comparison.

Our goal is to provide clear, reproducible metrics that shed light on how each model handles different task complexities and reasoning requirements.

## Benchmarks
Here is a closer look at each benchmark included in the leaderboard:

### Base Benchmarks
- **ARC-Easy / ARC-Challenge**: A set of multiple-choice science questions designed to measure a model’s scientific and commonsense reasoning.
- **DROP**: A reading comprehension benchmark emphasizing discrete reasoning steps.
- **WinoGrande**: A commonsense reasoning challenge focused on co-reference resolution.
- **GSM8K**: Grade-school math word problems testing arithmetic and multi-step reasoning.
- **HellaSwag**: A commonsense inference task centered on action completion.
- **HumanEval**: Evaluates code generation and reasoning in a programming context.
- **IFEval**: A specialized benchmark for incremental formal reasoning.
- **MATH**: High school-level math questions requiring detailed solutions.
- **MMLU / MMLU-Pro**: Multi-subject multiple-choice tests covering advanced high school and collegiate-level knowledge.
- **GPQA-Diamond**: A question-answering benchmark that assesses deeper reasoning and knowledge linking.
- **MMMU (Multiple-Choice / Open-Ended)**: A suite of multilingual and multi-domain tasks testing both structured and open-form responses.

### Agentic Benchmarks
- **GAIA**: Evaluates more autonomous or “agentic” reasoning, including planning and problem-solving.
- **GDM-InterCode-CTF**: A capture-the-flag style challenge focusing on code interpretation and generative debugging strategies.

---
"""

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"""
"""