Update README.md

README.md

@@ -1,3 +1,9 @@
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: sentence-similarity
+---
 ---
 ---
 
@@ -11,4 +17,43 @@ Read more about it in [the article](https://qdrant.tech/articles/minicoil).
 
 This model is designed to be used with [FastEmbed](https://github.com/qdrant/fastembed) library.
 
-ToDo
+> Note:
+This model is supposed to be used with Qdrant. Vectors have to be configured with [Modifier.IDF](https://qdrant.tech/documentation/concepts/indexing/?q=modifier#idf-modifier).
+
+```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"
+```