sentence embedding, sentence to vector using bert

refer to source code

.

#pip install -U sentence-transformers

#https://github.com/UKPLab/sentence-transformers

from sentence_transformers import SentenceTransformer, LoggingHandler

# Load Sentence model (based on BERT) from URL

model = SentenceTransformer('bert-base-nli-mean-tokens')

# Embed a list of sentences

sentences = ['This framework generates embeddings for each input sentence',

'Sentences are passed as a list of string.',

'The quick brown fox jumps over the lazy dog.']

sentence_embeddings = model.encode(sentences)

# The result is a list of sentence embeddings as numpy arrays

for sentence, embedding in zip(sentences, sentence_embeddings):

print("Sentence:", sentence)

print("Embedding:", embedding.shape, type(embedding))

print("")

.

result is like this:

Sentence: This framework generates embeddings for each input sentence

Embedding: (768,) <class 'numpy.ndarray'>

Sentence: Sentences are passed as a list of string.

Embedding: (768,) <class 'numpy.ndarray'>

Sentence: The quick brown fox jumps over the lazy dog.

Embedding: (768,) <class 'numpy.ndarray'>