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---
license: apache-2.0
language:
- en
pipeline_tag: sentence-similarity
base_model:
- jinaai/jina-embeddings-v2-small-en
---
# 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.
<img src="https://storage.googleapis.com/qdrant-examples/miniCOIL_inference.png" alt="miniCOIL inference" width="600"/>
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](https://qdrant.tech/articles/minicoil).
## Usage
This model is designed to be used with the [FastEmbed](https://github.com/qdrant/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](https://qdrant.tech/documentation/concepts/indexing/?q=modifier#idf-modifier). Otherwise, you'll have to personally calculate & scale the produced sparse representations by the IDF part of the BM25 formula.
```py
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"
``` |