shibing624/text2vec-base-chinese

Instalar y usar este modelo es fácil con text2vec:

pip install -U text2vec
from text2vec import SentenceModel
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
model = SentenceModel('shibing624/text2vec-base-chinese')
embeddings = model.encode(sentences)
print(embeddings)

Usar con Transformers de HuggingFace:

pip install transformers
from transformers import BertTokenizer, BertModel
import torch
# Mean Pooling - Tener en cuenta la máscara de atención para el promedio correcto
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] # El primer elemento de model_output contiene todas las incrustaciones de tokens
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Cargar modelo desde HuggingFace Hub
tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese')
model = BertModel.from_pretrained('shibing624/text2vec-base-chinese')
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
# Tokenizar oraciones
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Computar incrustaciones de tokens
with torch.no_grad():
    model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Incrustaciones de oraciones:")
print(sentence_embeddings)

Usar con sentence-transformers:

pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("shibing624/text2vec-base-chinese")
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
sentence_embeddings = m.encode(sentences)
print("Incrustaciones de oraciones:")
print(sentence_embeddings)