from sentence_transformers import SentenceTransformer

MODEL_NAME = 'bert-base-nli-mean-tokens'
MODEL = SentenceTransformer(MODEL_NAME)


def embed(sentence):
    return MODEL.encode(sentence).tolist()  # make it compatible with Elasticsearch

84093b508cfe96b4781604f31366a94827b74582
<mask token>

<mask token>


def embed(sentence):
    return MODEL.encode(sentence).tolist()

<mask token>
MODEL_NAME = 'bert-base-nli-mean-tokens'
MODEL = SentenceTransformer(MODEL_NAME)


def embed(sentence):
    return MODEL.encode(sentence).tolist()

from sentence_transformers import SentenceTransformer
MODEL_NAME = 'bert-base-nli-mean-tokens'
MODEL = SentenceTransformer(MODEL_NAME)


def embed(sentence):
    return MODEL.encode(sentence).tolist()

from sentence_transformers import SentenceTransformer

MODEL_NAME = 'bert-base-nli-mean-tokens'
MODEL = SentenceTransformer(MODEL_NAME)


def embed(sentence):
    return MODEL.encode(sentence).tolist()  # make it compatible with Elasticsearch
