Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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
+- **Named Entity Recognition**: Identifying organizations, financial metrics, economic indicators, etc.
+- **Sentiment Analysis**: Analyzing market sentiment in financial news and reports
+- **Question Answering**: Supporting research queries on economic literature
+- **Information Extraction**: Extracting structured data from unstructured economic texts
+
+### 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.