| | --- |
| | language: en |
| | license: mit |
| | library_name: transformers |
| | tags: |
| | - economics |
| | - finance |
| | - bert |
| | - language-model |
| | - financial-nlp |
| | - economic-analysis |
| | datasets: |
| | - custom_economic_corpus |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | pipeline_tag: text-classification |
| | --- |
| | # 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 |
| |
|
| | ```python |
| | 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: |
| |
|
| | ```bibtex |
| | @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. |
