Bert Base Turkish 128k Uncased Spelling Correction
This is a model based on sentence-transformers that can map sentences and paragraphs into a 16-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
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Release Time : 3/31/2023
Model Overview
This model is primarily used for sentence similarity calculation and feature extraction, capable of converting text into low-dimensional vector representations for subsequent machine learning task processing.
Model Features
Low-dimensional Vector Representation
Maps sentences and paragraphs into a 16-dimensional dense vector space for easy computation and processing.
Efficient Feature Extraction
Capable of quickly extracting text features, suitable for large-scale text processing tasks.
Sentence Similarity Calculation
Accurately calculates semantic similarity between sentences through distance metrics in the vector space.
Model Capabilities
Sentence Vectorization
Semantic Similarity Calculation
Text Feature Extraction
Text Clustering
Use Cases
Information Retrieval
Semantic Search
Improves the accuracy of search results by calculating the semantic similarity between queries and documents.
Enhances the relevance of search results and user satisfaction.
Text Analysis
Text Clustering
Automatically groups similar content for topic discovery or content organization.
Enables automated classification and management of text data.
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