WARNING: This is an outdated model. 🚀 Check out our new v3 small model, trained for improved inference speed, lighter footprint, and better semantic matching for caching. Redis semantic caching embedding model based on Alibaba NLP/gte modernbert base This is a sentence transformers model finetuned from Alibaba NLP/gte modernbert base on the Quora dataset. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching. Model Details Model Description Model Type: Sentence Transformer Base model: Alibaba NLP/gte modernbert base Maximum Sequence Length: 8192 tokens Output Dimensionality: 768 dimensions Similarity Function: Cosine Similarity Training Dataset: Quora Model Sources Documentation: Sentence Transformers Documentation Repository: Sentence Transformers on GitHub Hugging Face: Sentence Transformers on Hugging Face Full Model Architecture Usage First install the Sentence Transformers library: Then you can load this model and run inference. Binary Classification Metric Value : : cosine accuracy 0.90 cosine f1 0.87 cosine precision 0.84 cosine recall 0.90 cosine ap 0.92 Training Dataset Qu…
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