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("FPB", "F1", "FPB") task2 = Task("FiQA-SA", "F1", "FiQA-SA") task3 = Task("TSA", "RMSE", "TSA") task4 = Task("Headlines", "AvgF1", "Headlines") task5 = Task("FOMC", "F1", "FOMC") task7 = Task("FinArg-ACC", "MicroF1", "FinArg-ACC") task8 = Task("FinArg-ARC", "MicroF1", "FinArg-ARC") task9 = Task("MultiFin", "MicroF1", "Multifin") task10 = Task("MA", "MicroF1", "MA") task11 = Task("MLESG", "MicroF1", "MLESG") task12 = Task("NER", "EntityF1", "NER") task13 = Task("FINER-ORD", "EntityF1", "FINER-ORD") task14 = Task("FinRED", "F1", "FinRED") task15 = Task("SC", "F1", "SC") task16 = Task("CD", "F1", "CD") task17 = Task("FinQA", "EmAcc", "FinQA") task18 = Task("TATQA", "EmAcc", "TATQA") task19 = Task("ConvFinQA", "EmAcc", "ConvFinQA") task20 = Task("FNXL", "EntityF1", "FNXL") task21 = Task("FSRL", "EntityF1", "FSRL") task22 = Task("EDTSUM", "Rouge-1", "EDTSUM") task25 = Task("ECTSUM", "Rouge-1", "ECTSUM") task28 = Task("BigData22", "Acc", "BigData22") task30 = Task("ACL18", "Acc", "ACL18") task32 = Task("CIKM18", "Acc", "CIKM18") task34 = Task("German", "F1", "German") task36 = Task("Australian", "F1", "Australian") task38 = Task("LendingClub", "F1", "LendingClub") task40 = Task("ccf", "F1", "ccf") task42 = Task("ccfraud", "F1", "ccfraud") task44 = Task("polish", "F1", "polish") task46 = Task("taiwan", "F1", "taiwan") task48 = Task("portoseguro", "F1", "portoseguro") task50 = Task("travelinsurance", "F1", "travelinsurance") NUM_FEWSHOT = 0 # Change with your few shot # --------------------------------------------------- # Your leaderboard name TITLE = """

🐲 The FinBen FLARE Leaderboard

""" # What does your leaderboard evaluate? INTRODUCTION_TEXT = """ """ # Which evaluations are you running? how can people reproduce what you have? LLM_BENCHMARKS_TEXT = f""" ## Introduction 📊 The FinBen FLARE Leaderboard is designed to rigorously track, rank, and evaluate state-of-the-art models in financial Natural Language Understanding and Prediction. 📈 Unique to FLARE, our leaderboard not only covers standard NLP tasks but also incorporates financial prediction tasks such as stock movement and credit scoring, offering a more comprehensive evaluation for real-world financial applications. ## Metrics 📚 Our evaluation metrics include, but are not limited to, Accuracy, F1 Score, ROUGE score, BERTScore, and Matthews correlation coefficient (MCC), providing a multidimensional assessment of model performance. Metrics for specific tasks are as follows: FPB-F1 FiQA-SA-F1 TSA-RMSE Headlines-AvgF1 FOMC-F1 FinArg-ACC-MicroF1 FinArg-ARC-MicroF1 Multifin-MicroF1 MA-MicroF1 MLESG-MicroF1 NER-EntityF1 FINER-ORD-EntityF1 FinRED-F1 SC-F1 CD-F1 FinQA-EmAcc TATQA-EmAcc ConvFinQA-EmAcc FNXL-EntityF1 FSRL-EntityF1 EDTSUM-Rouge-1 ECTSUM-Rouge-1 BigData22-Acc ACL18-Acc CIKM18-Acc German-F1 Australian-F1 LendingClub-F1 ccf-F1 ccfraud-F1 polish-F1 taiwan-F1 portoseguro-F1 travelinsurance-F1 ## REPRODUCIBILITY 🔗 For more details, refer to our GitHub page [here](https://github.com/The-FinAI/PIXIU). """ 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""" @misc{xie2024finben, title={The FinBen: An Holistic Financial Benchmark for Large Language Models}, author={Qianqian Xie and Weiguang Han and Zhengyu Chen and Ruoyu Xiang and Xiao Zhang and Yueru He and Mengxi Xiao and Dong Li and Yongfu Dai and Duanyu Feng and Yijing Xu and Haoqiang Kang and Ziyan Kuang and Chenhan Yuan and Kailai Yang and Zheheng Luo and Tianlin Zhang and Zhiwei Liu and Guojun Xiong and Zhiyang Deng and Yuechen Jiang and Zhiyuan Yao and Haohang Li and Yangyang Yu and Gang Hu and Jiajia Huang and Xiao-Yang Liu and Alejandro Lopez-Lira and Benyou Wang and Yanzhao Lai and Hao Wang and Min Peng and Sophia Ananiadou and Jimin Huang}, year={2024}, eprint={2402.12659}, archivePrefix={arXiv}, primaryClass={cs.CL} } """