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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("anli_r1", "acc", "ANLI")
# task1 = Task("logiqa", "acc_norm", "LogiQA")
# retrieval tasks
mrr = Task("retrieval", "mrr", "MRR ⬆️")
map = Task("retrieval", "map", "MAP ⬆️")
# generation tasks
em = Task("generation", "em", "EM ⬆️")
f1 = Task("generation", "f1", "F1 ⬆️")
rouge1 = Task("generation", "rouge1", "Rouge-1 ⬆️")
rouge2 = Task("generation", "rouge2", "Rouge-2 ⬆️")
rougeL = Task("generation", "rougeL", "Rouge-L ⬆️")
accuracy = Task("generation", "accuracy", "ACC ⬆️")
completeness = Task("generation", "completeness", "COMP ⬆️")
hallucination = Task("generation", "hallucination", "HAL ⬇️")
utilization = Task("generation", "utilization", "UTIL ⬆️")
numerical_accuracy = Task("generation", "numerical_accuracy", "MACC ⬆️")
NUM_FEWSHOT = 0 # Change with your few shot
# ---------------------------------------------------
# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">🏅 OmniEval Leaderboard</h1>"""
# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
<div align="center">OmniEval: Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain</div>
"""
# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
# <div align="center">OmniEval: Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain</div>
<div align="center">
<!-- <a href="https://arxiv.org/abs/2405.13576" target="_blank"><img src=https://img.shields.io/badge/arXiv-b5212f.svg?logo=arxiv></a> -->
<!-- <a href="https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets/" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace%20Datasets-27b3b4.svg></a> -->
<!-- <a href="https://huggingface.co/ShootingWong/OmniEval-ModelEvaluator" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace%20Checkpoint-5fc372.svg></a> -->
<!-- <a href="https://huggingface.co/ShootingWong/OmniEval-HallucinationEvaluator" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace%20Checkpoint-b181d9.svg></a> -->
<a href="https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets/" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-27b3b4></a>
<a href="https://huggingface.co/ShootingWong/OmniEval-ModelEvaluator" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoint-5fc372></a>
<a href="https://huggingface.co/ShootingWong/OmniEval-HallucinationEvaluator" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoint-b181d9></a>
<a href="https://huggingface.co/spaces/NLPIR-RAG/OmniEval" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Leaderboard-blue></a>
<a href="https://github.com/RUC-NLPIR/FlashRAG/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green"></a>
<a><img alt="Static Badge" src="https://img.shields.io/badge/made_with-Python-blue"></a>
</div>
<!-- [](https://huggingface.co/spaces/Vchitect/VBench_Leaderboard) -->
<h4 align="center">
<p>
<a href="#wrench-installation">Installation</a> |
<!-- <a href="#sparkles-features">Features</a> | -->
<a href="#rocket-quick-start">Quick-Start</a> |
<a href="#bookmark-license">License</a> |
<a href="#star2-citation">Citation</a>
</p>
</h4>
<!--
With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components. -->
## 🔧 Installation
`conda env create -f environment.yml && conda activate finrag`
<!-- ## ✨ Features
1. -->
## 🚀 Quick-Start
Notion:
1. The code run path is `./OpenFinBench`
2. We provide our auto-generated evaluation dataset in <a href="https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets/" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-27b3b4></a>
### 1. Build the Retrieval Corpus
```
# cd OpenFinBench
sh corpus_builder/build_corpus.sh # Please see the annotation inner the bash file to set parameters.
```
### 2. Generate Evaluation Data Samples
1. Generate evaluation instances
```
# cd OpenFinBench
sh data_generator/generate_data.sh
```
2. Filter (quality inspection) evaluation instances
```
sh data_generator/generate_data_filter.sh
```
### 3. Inference Your Models
```
# cd OpenFinBench
sh evaluator/inference/rag_inference.sh
```
### 4. Evaluate Your Models
#### (a) Rule-based Evaluation
```
# cd OpenFinBench
sh evaluator/judgement/judger.sh # by setting judge_type="rule"
```
#### (b) Model-based Evalution
We propose five model-based metric: accuracy, completeness, utilization, numerical_accuracy, and hallucination. We have trained two models from Qwen2.5-7B by the lora strategy and human-annotation labels to implement model-based evaluation.
Note that the evaluator of hallucination is different from other four. Their model checkpoint can be load from the following huggingface links:
1. The evaluator for hallucination metric: <a href="https://huggingface.co/ShootingWong/OmniEval-HallucinationEvaluator" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoint-b181d9></a>
2. The evaluator for other metric: <a href="https://huggingface.co/ShootingWong/OmniEval-ModelEvaluator" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoint-5fc372></a>
To implement model-based evaluation, you can first set up two vllm servers by the following codes:
```
```
Then conduct the model-based evaluate using the following codes, (change the parameters inner the bash file).
```
sh evaluator/judgement/judger.sh
```
## 🔖 License
OmniEval is licensed under the [<u>MIT License</u>](./LICENSE).
## 🌟 Citation
The paper is waiting to be released!
<!-- # Check Infos
## Pipeline
1. Build corpus
2. Data generation
3. RAG inference
4. Result evaluatioin
## Code
1. remove "baichuan"
2. remove useless annotation -->
"""
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"""
"""
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