ONNX convert all MiniLM L6 v2 Conversion of sentence transformers/all MiniLM L6 v2 This is a sentence transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Usage (Sentence Transformers) Using this model becomes easy when you have sentence transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) 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. Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark : https://seb.sbert.net Background The project aims to train sentence embedding models on very large sentence level datasets using a self supervised contrastive learning objective. We used the pretrained nreimers/MiniLM L6 H384 uncased model and fine tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, wa…
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