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What are sentence transformers?
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning.
What is Siamese BERT?
BERT uses cross-encoder networks that take 2 sentences as input to the transformer network and then predict a target value. BERT is able to achieve SOTA performance on Semantic Textual Similarity tasks but both sentences must be passed through the full network.
What do you do with an embedded sentence?
Sentence Embeddings are very useful for many tasks in the language understanding domain, such as similarity or sentiment analysis. In Agent Assist, Sentence Embeddings are crucial for detecting the intent of the sentences said by the customer.
How are sentence transformers used in sentence embeddings?
The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings. The model is described in the paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Usage (HuggingFace Models Repository)
Which is the best sentence embedding model to use?
You can find recommended sentence embedding models here: SBERT.net – Pretrained Models This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed:
Which is the best sentence transformer in STS?
Performance Model STS benchmark Universal Sentence Encoder 74.92 Sentence Transformer Models (NLI + MNLI) nli-distilroberta-base-v2 84.38 nli-roberta-base-v2 85.54
How to install the sentence Transformers in Pip?
Install the sentence-transformers with pip: pip install -U sentence-transformers. Install from sources. Alternatively, you can also clone the latest version from the repository and install it directly from the source code: pip install -e . PyTorch with CUDA If you want to use a GPU / CUDA, you must install PyTorch with the matching CUDA Version.