The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
Paper • 2101.05667 • Published
How to use castorini/duot5-3b-msmarco with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="castorini/duot5-3b-msmarco") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("castorini/duot5-3b-msmarco")
model = AutoModel.from_pretrained("castorini/duot5-3b-msmarco")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This model is a T5-3B reranker, initialized with our pointwise ranker, castorini/monot5-3b-msmarco, and finetuned on the MS MARCO passage dataset for 50K steps (or 5 epochs) on the pairwise reranking task.
For more details on how to use it, check pygaggle.ai!
Paper describing the model: The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models