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Gbert Large Paraphrase Cosine Onnx
A German text embedding model based on sentence-transformers, mapping text to a 1024-dimensional vector space, specifically designed to enhance few-shot text classification performance in German
Downloads 18
Release Time : 10/23/2023
Model Overview
This model converts German sentences and paragraphs into 1024-dimensional dense vector representations, primarily used for sentence similarity computation and few-shot classification tasks. The ONNX version optimizes inference efficiency.
Model Features
High-dimensional Vector Representation
Generates 1024-dimensional dense vectors, effectively capturing semantic features of German text
Few-shot Optimization
Designed for few-shot learning scenarios, significantly improving classification performance when used with the SetFit framework
ONNX Runtime
Optimizes inference efficiency with ONNX format, suitable for production environment deployment
Domain-adaptive Training
Trained on Deutsche Telekom's German back-translation paraphrase dataset, delivering better performance on specialized domain texts
Model Capabilities
Sentence Embedding
Semantic Similarity Computation
Few-shot Text Classification
Paragraph Vectorization
Use Cases
Text Classification
Customer Feedback Classification
Automatically classifies a small amount of labeled customer feedback
Maintains high accuracy in few-shot scenarios
Information Retrieval
Semantic Search
Document retrieval system based on semantic similarity
Better understands user query intent compared to keyword search
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