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
How to fit this model
#2
by leatral - opened
From sbert cross encoder page, I find two train example, one, two. While one use CECorrelationEvaluator, two use CERerankingEvaluator.
I want use cross encoder to ranking the search items, which way should i use. From word meaning i think reranking maybe more suitable. But it's label only has 0/1. correlation's label could be number represent the relevance between query and doc.