ColBERT
Collection
Late interaction models • 10 items • Updated • 14
Model card for PyLate BERT Tiny
This is a PyLate model finetuned from google/bert_uncased_L-2_H-128_A-2 on the msmarco-bm25 dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
This model is primarily designed for unit tests in limited compute environments such as GitHub Actions. But it does work to an extent for basic use cases.
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Model tree for NeuML/pylate-bert-tiny
Base model
google/bert_uncased_L-2_H-128_A-2
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="NeuML/pylate-bert-tiny") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False)