Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. As my use case needs functionality for both English and Arabic, I am using the bert-base-multilingual-cased pretrained model. $\begingroup$ @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 . A metric like cosine similarity requires that the dimensions of the vector contribute equally and meaningfully, but this is not the case for BERT. Word embedding based doc2vec is still a good way to measure similarity between docs . GitHub statistics: Stars: Forks: ... networks like BERT / RoBERTa / XLM-RoBERTa etc. Translations: Chinese, Russian Progress has been rapidly accelerating in machine learning models that process language over the last couple of years. bert-as-service offers just that solution. In BERT training text is represented using three embeddings, Token Embeddings + Segment Embeddings + Position Embeddings. This progress has left the research lab and started powering some of the leading digital products. If you still want to use BERT, you have to either fine-tune it or build your own classification layers on top of it. I am using the HuggingFace Transformers package to access pretrained models. and are tuned specificially meaningul sentence embeddings such that sentences with similar meanings are close in vector space. BERT uses transformer architecture, an attention model to learn embeddings for words. You should consider Universal Sentence Encoder or InferSent therefore. You can use Sentence Transformers to generate the sentence embeddings. BERT consists of two pre training steps Masked Language Modelling (MLM) and Next Sentence Prediction (NSP). 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. Semantic Textual Similarity; Edit on GitHub; Semantic Textual Similarity¶ Once you have sentence embeddings computed, you usually want to compare them to each other. To answer your question, implementing it yourself from zero would be quite hard as BERT is not a trivial NN, but with this solution you can just plug it in into your algo that uses sentence similarity. 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. 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