MiniCOIL v1

MiniCOIL is a sparse neural embedding model for textual retrieval.

It creates 4-dimensional embeddings for each word stem, capturing the word's meaning.
These meaning embeddings are combined into a bag-of-words (BoW) representation of the input text.
The final sparse representation is calculated by weighting each word using the BM25 scoring formula.

In the case of a word's absence in the miniCOIL vocabulary, word weight in sparse representation is purely based on the BM25 score.

Read more about miniCOIL in the article.

Usage

This model is designed to be used with the FastEmbed library.

Note: This model was designed with Qdrant's specifics in mind; miniCOIL sparse vectors in Qdrant have to be configured with Modifier.IDF. Otherwise, you'll have to personally calculate & scale the produced sparse representations by the IDF part of the BM25 formula.

from fastembed import SparseTextEmbedding

model = SparseTextEmbedding(model_name="Qdrant/minicoil-v1")

documents = [
    "fruit bat",
    "baseball bat",
]

embeddings = list(model.embed(documents))
query_embedding = list(model.query_embed("bat in a cave"))

# embeddings[0] - "fruit bat"
# SparseEmbedding(values=array([-1.2509683 , -0.9510568 , -0.55398935,  0.188206  ,  1.0497165 ,
#       0.31841373, -0.82047373, -0.9671025 ], dtype=float32), indices=array([ 8992,  8993,  8994,  8995, 18832, 18833, 18834, 18835],
#      dtype=int32)) # 8992,  8993,  8994,  8995 - 4D "fruit" representation, 18832, 18833, 18834, 18835 - 4D "bat" representation

# embeddings[1] - "baseball bat"
#SparseEmbedding(values=array([ 1.1004512 , -0.5959816 ,  0.23380531, -1.0912857 ,  1.6768292 ],
#      dtype=float32), indices=array([     18832,      18833,      18834,      18835, 2068153269],
#      dtype=int32)) # 18832, 18833, 18834, 18835 - 4D "bat" representation, 2068153269 - 1D "baseball" representation, as "baseball" is not in miniCOIL_v1 vocabulary, so we fall back to Qdrant/bm25 1D score
 
# query_embedding - "bat in a cave"
#[SparseEmbedding(values=array([ 0.5656684 ,  0.395691  , -0.48945513, -0.5328054 , -0.5889519 ,
#         0.55871224,  0.27323055,  0.5160634 ], dtype=float32), indices=array([18832, 18833, 18834, 18835, 18920, 18921, 18922, 18923],
#       dtype=int32))] # 18832, 18833, 18834, 18835 - 4D "bat" representation, 18920, 18921, 18922, 18923 - 4D "cave" representation, "in"/"a" - removed stop words

bat_1 = embeddings[0].values[4:8]
bat_2 = embeddings[1].values[:4]
bat_query = query_embedding[0].values[:4]

dot_product_1 = (bat_1 * bat_query).sum() #np.float32(1.6366475) measuring dot product between matching indices of sparse vectors
dot_product_2 = (bat_2 * bat_query).sum() #np.float32(0.8536716) measuring dot product between matching indices of sparse vectors

#1.6366475 > 0.8536716, as "bat" in "fruit bat" is more semantically similar to "bat" in "bat in a cave", as "bat" in "baseball bat"