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  - sentence-transformers
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  - sentence-similarity
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  - feature-extraction
 
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  widget: []
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  ---
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- # SentenceTransformer
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- This is a [sentence-transformers](https://www.SBERT.net) model trained. 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.
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- ## Model Details
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- ### Model Description
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- - **Model Type:** Sentence Transformer
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- <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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- - **Maximum Sequence Length:** 512 tokens
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- - **Output Dimensionality:** 768 tokens
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- - **Similarity Function:** Cosine Similarity
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- <!-- - **Training Dataset:** Unknown -->
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- <!-- - **Language:** Unknown -->
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- <!-- - **License:** Unknown -->
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- ### Model Sources
 
 
 
 
 
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- - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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  ### Full Model Architecture
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  - Datasets: 2.21.0
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  - Tokenizers: 0.19.1
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  ## Citation
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  ### BibTeX
 
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  - sentence-transformers
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  - sentence-similarity
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  - feature-extraction
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+ - UniHGKR
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  widget: []
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  ---
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+ # UniHGKR-base-beir
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+ The UniHGKR-base-beir model is derived from the UniHGKR-base model, further fine-tuned on MS MARCO for evaluation on the BEIR benchmark. We recommend using the [sentence-transformers](https://www.SBERT.net) package to load our model and to perform embedding for paragraphs and sentences.
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+ 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.
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+ ## Evaluation on BEIR
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+ The evaluation code can be found at https://github.com/ZhishanQ/UniHGKR.
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+ ## Model Details
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  ### Full Model Architecture
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  - Datasets: 2.21.0
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  - Tokenizers: 0.19.1
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+ ### Sentence Transformers Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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  ## Citation
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  ### BibTeX