# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tuhailong/cross-encoder-bert-base")
model = AutoModelForSequenceClassification.from_pretrained("tuhailong/cross-encoder-bert-base")Quick Links
Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.
Model
model created by sentence-tansformers，model struct is cross-encoder
Usage
>>> from sentence_transformers.cross_encoder import CrossEncoder
>>> model = CrossEncoder('tuhailong/cross-encoder')
>>> scores = model.predict([["今天天气不错", "今天心情不错"]])
>>> print(scores)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tuhailong/cross-encoder-bert-base")