| --- |
| 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: fill-mask |
| --- |
| |
| # EconBERT |
|
|
| ## Model Description |
|
|
| EconBERT is a BERT-based language model specifically fine-tuned for economic and financial text analysis. 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 |
| - Performance may vary on highly technical economic sub-domains not well-represented in the training data |
| - For detailed discussion of limitations, please refer to our paper |
|
|
| ## Training Data |
|
|
| EconBERT was trained on a large corpus of economic and financial texts. 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. |
