Nirman
Qdrant

Qdrant/all_miniLM_L6_v2_with_attentions

ONNX port of sentence-transformers/all-MiniLM-L6-v2 adjusted to return attention weights....

Model Documentation

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.

py
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


                    ]))


]


Files & Weights

FilenameSizeAction
model.onnx 0.08 GB