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from dataclasses import dataclass |
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from enum import Enum |
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@dataclass |
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class Task: |
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benchmark: str |
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metric: str |
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col_name: str |
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class Tasks(Enum): |
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task0 = Task("FPB", "F1", "FPB") |
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task2 = Task("FiQA-SA", "F1", "FiQA-SA") |
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task3 = Task("TSA", "RMSE", "TSA") |
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task4 = Task("Headlines", "AvgF1", "Headlines") |
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task5 = Task("FOMC", "F1", "FOMC") |
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task7 = Task("FinArg-ACC", "MicroF1", "FinArg-ACC") |
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task8 = Task("FinArg-ARC", "MicroF1", "FinArg-ARC") |
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task9 = Task("MultiFin", "MicroF1", "Multifin") |
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task10 = Task("MA", "MicroF1", "MA") |
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task11 = Task("MLESG", "MicroF1", "MLESG") |
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task12 = Task("NER", "EntityF1", "NER") |
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task13 = Task("FINER-ORD", "EntityF1", "FINER-ORD") |
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task14 = Task("FinRED", "F1", "FinRED") |
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task15 = Task("SC", "F1", "SC") |
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task16 = Task("CD", "F1", "CD") |
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task17 = Task("FinQA", "EmAcc", "FinQA") |
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task18 = Task("TATQA", "EmAcc", "TATQA") |
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task19 = Task("ConvFinQA", "EmAcc", "ConvFinQA") |
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task20 = Task("FNXL", "EntityF1", "FNXL") |
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task21 = Task("FSRL", "EntityF1", "FSRL") |
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task22 = Task("EDTSUM", "Rouge-1", "EDTSUM") |
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task25 = Task("ECTSUM", "Rouge-1", "ECTSUM") |
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task28 = Task("BigData22", "Acc", "BigData22") |
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task30 = Task("ACL18", "Acc", "ACL18") |
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task32 = Task("CIKM18", "Acc", "CIKM18") |
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task34 = Task("German", "F1", "German") |
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task36 = Task("Australian", "F1", "Australian") |
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task38 = Task("LendingClub", "F1", "LendingClub") |
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task40 = Task("ccf", "F1", "ccf") |
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task42 = Task("ccfraud", "F1", "ccfraud") |
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task44 = Task("polish", "F1", "polish") |
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task46 = Task("taiwan", "F1", "taiwan") |
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task48 = Task("portoseguro", "F1", "portoseguro") |
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task50 = Task("travelinsurance", "F1", "travelinsurance") |
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NUM_FEWSHOT = 0 |
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TITLE = """<h1 align="center" id="space-title">π² The FinBen FLARE Leaderboard</h1>""" |
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INTRODUCTION_TEXT = """π The FinBen FLARE Leaderboard is designed to rigorously track, rank, and evaluate state-of-the-art models in financial Natural Language Understanding and Prediction. |
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π 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. |
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π 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. |
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π For more details, refer to our GitHub page [here](https://github.com/The-FinAI/PIXIU). |
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""" |
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LLM_BENCHMARKS_TEXT = f""" |
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## How it works |
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## Reproducibility |
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To reproduce our results, here is the commands you can run: |
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""" |
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EVALUATION_QUEUE_TEXT = """ |
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## Some good practices before submitting a model |
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### 1) Make sure you can load your model and tokenizer using AutoClasses: |
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```python |
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from transformers import AutoConfig, AutoModel, AutoTokenizer |
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config = AutoConfig.from_pretrained("your model name", revision=revision) |
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model = AutoModel.from_pretrained("your model name", revision=revision) |
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tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision) |
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``` |
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If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded. |
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Note: make sure your model is public! |
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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! |
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### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index) |
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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`! |
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### 3) Make sure your model has an open license! |
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This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model π€ |
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### 4) Fill up your model card |
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When we add extra information about models to the leaderboard, it will be automatically taken from the model card |
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## In case of model failure |
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If your model is displayed in the `FAILED` category, its execution stopped. |
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Make sure you have followed the above steps first. |
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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). |
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""" |
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CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" |
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CITATION_BUTTON_TEXT = r""" |
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""" |
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