check encoder outputs

toy.py

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': [
        '买卖合同纠纷 裁定书',
        '裁定书',
        '买卖合同纠纷'],
    'matches': [
        '买卖合同纠纷一审行政裁定书',
        '买卖合同纠纷二审行政裁定书',
        '买卖合同纠纷二审行政判决书',
        '一审被告金英玉买卖合同纠纷一案',
        '陈日瑛与房屋买卖合同纠纷二审民事裁定书',
        '买卖合同纠纷',
        '买卖合同纠纷裁定书',
        '买卖合同纠纷 裁定书',
        '买卖合同纠纷   裁定书',
        '劳动合同纠纷 裁定书',
        '裁判书',
        '审判书'
    ]}

target_case = case_1
docs = DocumentArray([Document(text=t) for t in target_case['matches']])
q_docs = DocumentArray([Document(text=t) for t in target_case['query']])
encoder.encode(docs=q_docs, parameters={})
encoder.encode(docs=docs, parameters={})
q_docs.match(docs, metric='cosine', limit=10)

for q in q_docs:
    print('-'*20)
    print(f'query: {q.text}')
    for m in q.matches:
        print(f'match: {m.text}, {m.scores["cosine"].value}')