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Industry Bert Sec V0.1

Developed by llmware
A BERT sentence vector transformation model optimized for the financial and regulatory domain, trained on SEC documents
Downloads 8,587
Release Time : 9/29/2023

Model Overview

A 768-dimensional sentence vector transformation model based on BERT architecture, fine-tuned for the financial and regulatory domain, suitable for semantic embedding tasks

Model Features

Domain optimization
Fine-tuned specifically for the financial and regulatory domain (SEC documents)
Plug-and-play
Can be used as a direct replacement for embedding tasks in financial regulation
Self-supervised training
Utilizes custom self-supervised processes and hybrid contrastive learning techniques

Model Capabilities

Generate semantic embedding vectors
Financial document analysis
Regulatory document processing

Use Cases

Financial regulation
SEC document analysis
Process and analyze U.S. Securities and Exchange Commission regulatory documents
Financial document similarity comparison
Calculate semantic similarity between financial documents
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