Instructions to use tuhailong/cross-encoder-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuhailong/cross-encoder-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tuhailong/cross-encoder-bert-base")# 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") - Notebooks
- Google Colab
- Kaggle
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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