SentenceTransformer based on distilbert/distilbert base uncased This is a sentence transformers model finetuned from distilbert/distilbert base uncased. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. Model Details Model Description Model Type: Sentence Transformer Base model: distilbert/distilbert base uncased Maximum Sequence Length: 512 tokens Output Dimensionality: 768 dimensions Similarity Function: Cosine Similarity Model Sources Documentation: Sentence Transformers Documentation Repository: Sentence Transformers on GitHub Hugging Face: Sentence Transformers on Hugging Face Full Model Architecture Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library: Then you can load this model and run inference. Click to see the direct usage in Transformers Click to expand Training Details Framework Versions Python: 3.12.8 Sentence Transformers: 5.1.0 Transformers: 4.55.4 PyTorch: 2.8.0 Accelerate: 1.5.2 Datasets: 3.5.0 Tokenizers: 0.21.1 Citation BibTeX
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