distilbert-base-financial-relation-extraction
| Property | Value |
|---|---|
| Author | yseop |
| Model Type | DistilBERT-base transformer |
| Task | Financial Relation Extraction |
| Model URL | Hugging Face |
What is distilbert-base-financial-relation-extraction?
FReE (Financial Relation Extraction) is a specialized DistilBERT model fine-tuned for detecting and classifying relationships between financial terms. Built on the DistilBERT architecture, this model has been trained on a comprehensive dataset of financial definitions from sources like Wikimedia, IFRS, and Investopedia. The model excels at identifying five key relationship types: no relationship (x), has, is in, is, and are.
Implementation Details
The model utilizes the DistilBERT-base architecture from Hugging Face, with additional pretraining to optimize weights for financial domain tasks. It processes financial text to identify relationship patterns between terms, achieving impressive metrics with a weighted average F1-score of 0.8243 across all relationship types.
- Custom financial dataset preprocessing using Stanford Open Information Extraction
- Specialized relationship classification across 5 categories
- Pretrained weight initialization for optimal performance
- Balanced evaluation metrics across relationship types
Core Capabilities
- Relationship detection between financial terms
- High precision (83.52%) and recall (82.51%) for relationship classification
- Particularly strong performance in detecting no-relationship cases (88.67% F1-score)
- Effective processing of complex financial definitions and statements
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically designed for financial domain relationship extraction, with custom training on financial definitions and impressive performance metrics across different relationship types. Its specialized focus on financial terminology and relationships makes it particularly valuable for fintech applications.
Q: What are the recommended use cases?
The model is ideal for financial document analysis, automated financial knowledge extraction, regulatory compliance checking, and financial term relationship mapping. It's particularly useful for processing financial definitions, statements, and documentation where understanding relationships between terms is crucial.
