# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tuhailong/cross_encoder_roberta-wwm-ext-large")
model = AutoModelForSequenceClassification.from_pretrained("tuhailong/cross_encoder_roberta-wwm-ext-large")Quick Links
Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
Model
model created by sentence-tansformers，model struct is cross-encoder，pretrained model is hfl/chinese-roberta-wwm-ext-large.
Code
train code from https://github.com/TTurn/cross-encoder
Usage
>>> from sentence_transformers.cross_encoder import CrossEncoder
>>> model = CrossEncoder(model_save_path, device="cuda", max_length=64)
>>> sentences = ["今天天气不错", "今天心情不错"]
>>> score = model.predict([sentences])
>>> print(score[0])
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tuhailong/cross_encoder_roberta-wwm-ext-large")