FOMC RoBERTa

🚀 联邦公开市场委员会鹰派 - 鸽派 - 中立分类任务微调模型

本页面包含ACL 2023论文 "万亿美元词汇：全新金融数据集、任务与市场分析" 所使用的模型。这项工作由佐治亚理工学院金融服务创新实验室完成。该金融科技实验室是美国东南部金融教育、研究和行业的中心。

论文可在 SSRN 上获取。

🚀 快速开始

✨ 主要特性

本模型用于联邦公开市场委员会（FOMC）声明的鹰派 - 鸽派 - 中立分类任务，能够有效帮助金融从业者和研究者分析货币政策立场。

📦 安装指南

本项目使用Python代码进行模型调用，需要安装transformers库。

💻 使用示例

基础用法

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig

tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/FOMC-RoBERTa", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/FOMC-RoBERTa", num_labels=3)
config = AutoConfig.from_pretrained("gtfintechlab/FOMC-RoBERTa")

classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, device=0, framework="pt")
results = classifier(["Such a directive would imply that any tightening should be implemented promptly if developments were perceived as pointing to rising inflation.", 
                      "The International Monetary Fund projects that global economic growth in 2019 will be the slowest since the financial crisis."], 
                      batch_size=128, truncation="only_first")

print(results)

📚 详细文档

标签解释

数据集

所有带训练 - 测试划分的标注数据集（3个随机种子）可在 GitHub页面 上获取。

📄 许可证

本项目采用 CC BY - NC 4.0 许可证。

🔗 引用与联系信息

引用

如果您使用了本项目的代码、数据或模型，请引用我们的论文：

@inproceedings{shah-etal-2023-trillion,
    title = "Trillion Dollar Words: A New Financial Dataset, Task {\&} Market Analysis",
    author = "Shah, Agam  and
      Paturi, Suvan  and
      Chava, Sudheer",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.368",
    doi = "10.18653/v1/2023.acl-long.368",
    pages = "6664--6679",
    abstract = "Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a major driver of financial market returns. We construct the largest tokenized and annotated dataset of FOMC speeches, meeting minutes, and press conference transcripts in order to understand how monetary policy influences financial markets. In this study, we develop a novel task of hawkish-dovish classification and benchmark various pre-trained language models on the proposed dataset. Using the best-performing model (RoBERTa-large), we construct a measure of monetary policy stance for the FOMC document release days. To evaluate the constructed measure, we study its impact on the treasury market, stock market, and macroeconomic indicators. Our dataset, models, and code are publicly available on Huggingface and GitHub under CC BY-NC 4.0 license.",
}

联系信息

如有任何问题，请联系 Agam Shah（ashah482[at]gatech[dot]edu）。
GitHub: @shahagam4 个人网站: https://shahagam4.github.io/