Bert MLM arXiv MP class zbMath 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. The model is specifically designed to compute similarities of short mathematical texts. 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. Background Intended uses Our model is intended to be used as a sentence and short paragraph encoder for mathematical texts. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. Training procedure Domain adaption We use the domain adapted math similarity/Bert MLM arXiv model. Please refer to the model card for more de…
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