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all_miniLM_L6_v2_with_attentions
Qdrant/all_miniLM_L6_v2_with_attentions
all_miniLM_L6_v2_with_attentions is a sentence similarity model from Qdrant. Use it when you need a score for how close two texts are. It is set up for transformers. The card lists the license as apache-2.0.
ONNX port of sentence-transformers/all-MiniLM-L6-v2 adjusted to return attention weights.
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At a glance
- Task
- Sentence Similarity
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- May 9, 2024
- Updated
- Sep 27, 2026
- SHA
- e6e0a7e8
Model card
From the Hugging Face model README
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
# ]))
# ]
- Task
- Sentence Similarity
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Languages
- en
- Created
- May 9, 2024
- Updated
- Sep 27, 2026