beethogedeon / SentEconBert

SentEconBERT

Model Description

SentEconBERT is a EconBERT-based language model specifically fine-tuned for sentiment analysis on economic and financial text. The model is designed to capture domain-specific language patterns, terminology, and contextual relationships in economic literature, research papers, financial reports, and related documents.

Note : The complete details of model architecture, training methodology, evaluation, and performance metrics are available in our paper. Please refer to the citation section below.

Intended Uses & Limitations

Intended Uses

• Economic Text Classification : Categorizing economic documents, papers, or news articles
• Sentiment Analysis : Analyzing market sentiment in financial news and reports
• Information Extraction : Extracting structured data from unstructured economic texts
• etc.

Limitations

• The model is specialized for economic and financial domains and may not perform as well on general text
• For detailed discussion of limitations, please refer to our paper

Training Data

SentEconBERT was trained on the FinancialPhraseBank dataset. For comprehensive information about the training data, including sources, size, preprocessing steps, and other details, please refer to our paper.

Evaluation Results

We evaluated EconBERT on several economic NLP tasks and compared its performance with general-purpose and other domain-specific models. The detailed evaluation methodology and complete results are available in our paper.

Key findings include:

• Improved performance on economic domain tasks compared to general BERT models
• State-of-the-art results on [specific tasks, if applicable]
• [Any other high-level results worth highlighting]

How to Use

from transformers import AutoTokenizer, AutoModel
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("YourUsername/EconBERT")
model = AutoModel.from_pretrained("YourUsername/EconBERT")
# Example usage
text = "The Federal Reserve increased interest rates by 25 basis points."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

For task-specific fine-tuning and applications, please refer to our paper and the examples provided in our GitHub repository.

Citation

If you use EconBERT in your research, please cite our paper:

@article{LastName2025econbert,
  title={EconBERT: A Large Language Model for Economics},
  author={Zhang, Philip and Rojcek, Jakub and Leippold, Markus},
  journal={SSRN Working Paper},
  year={2025},
  volume={},
  pages={},
  publisher={University of Zurich},
  doi={}
}

Additional Information

• Model Type : BERT
• Language(s) : English
• License : MIT

For more detailed information about model architecture, training methodology, evaluation results, and applications, please refer to our paper.