BERT is not trained for semantic sentence similarity directly. I am using the HuggingFace Transformers package to access pretrained models. BERT consists of two pre training steps Masked Language Modelling (MLM) and Next Sentence Prediction (NSP). bert-as-service offers just that solution. In BERT training text is represented using three embeddings, Token Embeddings + Segment Embeddings + Position Embeddings. Jacob Devlin (one of the authors of the BERT paper) wrote: I'm not sure what these vectors are, since BERT does not generate meaningful sentence vectors. and are tuned specificially meaningul sentence embeddings such that sentences with similar meanings are close in vector space. A great example of this is the recent announcement of how the BERT model is now a major force behind Google Search. You should consider Universal Sentence Encoder or InferSent therefore. I need to be able to compare the similarity of sentences using something such as cosine similarity. Word embedding based doc2vec is still a good way to measure similarity between docs . $\begingroup$ @zachdji thanks for the information .Can you share the syntax for mean pool and max pool i tired torch.mean(hidden_reps[0],1) but when i tried to find cosin similarity for 2 different sentences it gave me high score .So not sure whether im doing the right way to get the sentence embedding . Semantic Textual Similarity; Edit on GitHub; Semantic Textual Similarity¶ Once you have sentence embeddings computed, you usually want to compare them to each other. BERT uses transformer architecture, an attention model to learn embeddings for words. These embeddings are much more meaningful as compared to the one obtained from bert-as-service, as they have been fine-tuned such that semantically similar sentences have higher similarity score. To add to @jindřich answer, BERT is meant to find missing words in a sentence and predict next sentence. A metric like cosine similarity requires that the dimensions of the vector contribute equally and meaningfully, but this is not the case for BERT. This progress has left the research lab and started powering some of the leading digital products. IJCNLP 2019 • UKPLab/sentence-transformers • However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10, 000 sentences requires about 50 million inference computations (~65 hours) with BERT. Here, I show you how you can compute the cosine similarity between embeddings, for example, to measure the semantic similarity … GitHub statistics: Stars: Forks: ... networks like BERT / RoBERTa / XLM-RoBERTa etc. If you still want to use BERT, you have to either fine-tune it or build your own classification layers on top of it. 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