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.
Note: This model is supposed to be used with Qdrant. Vectors have to be configured with Modifier.IDF.
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(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
# ]))
# ]