nan-wang

from transformer_tf_text_encode import TransformerTFTextEncoder
from jina import Document, DocumentArray

encoder = TransformerTFTextEncoder(
    pretrained_model_name_or_path='hfl/chinese-legal-electra-small-generator',
    pooling_strategy='cls'
)

case_1 = {
    'query': [

nan-wang

from jina import Executor, Document, DocumentArray
encoder = Executor.from_hub('jinahub://TransformerTorchEncoder')
da = DocumentArray([
    Document(text='Jina is a neural search framework.'),
    Document(text='Jina relies heavily on multiprocessing.'),
    Document(text='Jina is backed by Jina AI.')])

encoder.encode(docs=da)
for doc in da:
    print(f'{doc.embedding}')