metadata
license: apache-2.0
language:
- en
pipeline_tag: sentence-similarity
ONNX port of sentence-transformers/all-MiniLM-L6-v2 adjusted to return attention weights.
This model is intended to be used for BM42 searches.
Usage
Here's an example of performing inference using the model with FastEmbed.
from fastembed import SparseTextEmbedding
documents = [
"You should stay, study and sprint.",
"History can only prepare us to be surprised yet again.",
]
model = SparseTextEmbedding(model_name="Qdrant/bm42-all-minilm-l6-v2-attentions")
embeddings = list(embedding_model.embed(documents))
# [
# SparseEmbedding(values=array([0.26399775, 0.24662513, 0.47077307]),
# indices=array([1881538586, 150760872, 1932363795])),
# SparseEmbedding(values=array(
# [0.38320042, 0.25453135, 0.18017513, 0.30432631, 0.1373556]),
# indices=array([
# 733618285, 1849833631, 1008800696, 2090661150,
# 1117393019
# ]))
# ]