---
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.