How do you calculate similarity using BERT?
BERT For Measuring Text Similarity
- Take a sentence, convert it into a vector.
- Take many other sentences, and convert them into vectors.
- Find sentences that have the smallest distance (Euclidean) or smallest angle (cosine similarity) between them — more on that here.
How can you represent to calculate cosine similarity?
2.4.7 Cosine Similarity A document can be represented by thousands of attributes, each recording the frequency of a particular word (such as a keyword) or phrase in the document. Thus, each document is an object represented by what is called a term-frequency vector.
Can BERT learn Subword Embeddings?
BERT does not provide word-level representations, but subword representations. You may want to combine the vectors of all subwords of the same word (e.g. by averaging them), but that is up to you, BERT only gives you the subword vectors. Subwords are used for representing both the input text and the output tokens.
What are BERT Embeddings?
Sentence Embeddings is just a numeric class to distinguish between sentence A and B. As discussed, BERT base model uses 12 layers of transformer encoders, each output per token from each layer of these can be used as a word embedding!
How do you measure similarity between sentences?
The easiest way of estimating the semantic similarity between a pair of sentences is by taking the average of the word embeddings of all words in the two sentences, and calculating the cosine between the resulting embeddings.
Can you use Bert to measure cosine distance?
Therefore, BERT embeddings cannot be used directly to apply cosine distance to measure similarity. However, there are easy wrapper services and implementations like the popular bert-as-a-service that can be used to that effect.
How to compare sentence similarity using embeddings from Bert?
Alternatively you can take the average vector of the sequence (like you say over the first (?) axis), which can yield better results according to the huggingface documentation (3rd tip). Note that BERT was not designed for sentence similarity using the cosine distance, though in my experience it does yield decent results.
Can you use Bert to measure semantic similarity?
An important note here is that BERT is not trained for semantic sentence similarity directly like the Universal Sentence Encoder or InferSent models. Therefore, BERT embeddings cannot be used directly to apply cosine distance to measure similarity.
How to compare two vectors with cosine similarity?
The shapes output are [1, n, vocab_size], where n can have any value. In order to compute two vectors’ cosine similarity, they need to be the same length. How can I do this here?