mandarjoshi/trivia_qa
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How to use metarank/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("metarank/all-MiniLM-L6-v2")
sentences = [
"The weather is lovely today.",
"It's so sunny outside!",
"He drove to the stadium."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. The ONNX version of this model is made for the Metarank re-ranker to do semantic similarity.
Check out the main Metarank docs on how to configure it.
TLDR:
- type: field_match
name: title_query_match
rankingField: ranking.query
itemField: item.title
distance: cos
method:
type: bert
model: metarank/all-MiniLM-L6-v2
$> pip install -r requirements.txt
$> python convert.py
============= Diagnostic Run torch.onnx.export version 2.0.0+cu117 =============
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================
Apache 2.0