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Modernbert Embed Base Ft Sts Spanish Matryoshka 768 64

Developed by mrm8488
This is a sentence transformer fine-tuned from the modernbert-embed-base model for generating sentence embeddings and calculating semantic similarity.
Downloads 443
Release Time : 1/10/2025

Model Overview

The model can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like semantic text similarity, semantic search, paraphrase mining, text classification, and clustering.

Model Features

High-dimensional semantic representation
Can map text to a 768-dimensional vector space to capture deep semantic features
Multidimensional similarity calculation
Supports semantic similarity calculation at different dimensions (768/512/256/128/64)
Long text processing
Maximum sequence length of 8192 tokens, suitable for processing long texts
Efficient fine-tuning
Fine-tuned on private STS datasets to improve performance on semantic similarity tasks

Model Capabilities

Semantic text similarity calculation
Semantic search
Paraphrase mining
Text classification
Text clustering

Use Cases

Information retrieval
Similar document retrieval
Retrieve relevant documents by calculating document vector similarity
Content recommendation
Related content recommendation
Recommend related content to users based on semantic similarity
Q&A systems
Similar question matching
Match semantically similar questions in Q&A systems
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