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英文字典中文字典相关资料:


  • SentenceTransformers Documentation — Sentence . . .
    Sentence Transformers (a k a SBERT) is the go-to Python module for accessing, using, and training state-of-the-art embedding and reranker models
  • sentence-transformers (Sentence Transformers) - Hugging Face
    In the following you find models tuned to be used for sentence text embedding generation They can be used with the sentence-transformers package
  • SentenceTransformer — Sentence Transformers documentation
    Loads or creates a SentenceTransformer model that can be used to map sentences text to embeddings Parameters: model_name_or_path (str, optional) – If it is a filepath on disk, it loads the model from that path If it is not a path, it first tries to download a pre-trained SentenceTransformer model
  • Using Sentence Transformers at Hugging Face
    sentence-transformers is a library that provides easy methods to compute embeddings (dense vector representations) for sentences, paragraphs and images Texts are embedded in a vector space such that similar text is close, which enables applications such as semantic search, clustering, and retrieval
  • Usage — Sentence Transformers documentation - SBERT. net
    Characteristics of Sentence Transformer (a k a bi-encoder) models: Calculates a fixed-size vector representation (embedding) given texts or images Embedding calculation is often efficient, embedding similarity calculation is very fast
  • Using Sentence Transformers at Hugging Face · Hugging Face
    Texts are embedded in a vector space such that similar text is close, which enables applications such as semantic search, clustering, and retrieval You can find over 500 hundred sentence-transformer models by filtering at the left of the models page
  • Computing Embeddings — Sentence Transformers . . .
    Even though we talk about sentence embeddings, you can use Sentence Transformers for shorter phrases as well as for longer texts with multiple sentences See Input Sequence Length for notes on embeddings for longer texts The first step is to load a pretrained Sentence Transformer model
  • sentence-transformers all-MiniLM-L6-v2 · Hugging Face
    Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings
  • Quickstart — Sentence Transformers documentation . . .
    Characteristics of Sentence Transformer (a k a bi-encoder) models: Calculates a fixed-size vector representation (embedding) given texts or images Embedding calculation is often efficient, embedding similarity calculation is very fast
  • sentence-transformers stsb-xlm-r-multilingual · Hugging Face
    Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings
  • Modules — Sentence Transformers documentation - SBERT. net
    Performs pooling (max or mean) on the token embeddings Using pooling, it generates from a variable sized sentence a fixed sized sentence embedding This layer also allows to use the CLS token if it is returned by the underlying word embedding model You can concatenate multiple poolings together





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