Edit model card

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. This model structure is as same as tuhailong/cross_encoder_roberta-wwm-ext_v1,the difference is changing the epoch from 5 to 1, the performance is better in my dataset.

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])

Code

train code from https://github.com/TTurn/cross-encoder

Downloads last month
30
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.