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Finance2 Embedding Small En V1.5

Developed by baconnier
This is a sentence embedding model fine-tuned on financial datasets based on BAAI/bge-small-en-v1.5, designed for semantic text similarity, semantic search, and related tasks.
Downloads 2,120
Release Time : 6/9/2024

Model Overview

The model maps sentences and paragraphs into a 384-dimensional dense vector space, particularly suitable for financial text processing tasks such as semantic similarity calculation, text classification, and clustering analysis.

Model Features

Financial Domain Optimization
Fine-tuned on professional financial datasets for better understanding of financial terms and concepts
Efficient Vector Representation
Converts text into 384-dimensional dense vectors, suitable for large-scale semantic search
Multiple Similarity Metrics Support
Supports various similarity calculation methods including cosine, dot product, Manhattan, and Euclidean distances

Model Capabilities

Semantic Text Similarity Calculation
Financial Text Feature Extraction
Semantic Search
Text Classification
Clustering Analysis

Use Cases

Financial Information Retrieval
Financial Q&A System
Used to match user financial questions with the most relevant answers in the knowledge base
High-accuracy semantic matching
Financial Document Processing
Financial Document Clustering
Automatically classifies and organizes large volumes of financial documents
Improved document management efficiency
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