[DOCS] Usage example in readme

README.md

@@ -1,17 +1,24 @@
 ---
 language:
 - en
-pipeline_tag: fill-mask
+pipeline_tag: sentence-similarity
+tags:
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - transformers
+  - domain-specific
 widget:
-- text: "Marshmallon should be [MASK]. It allows a 1 card FTK."
+- text: Marshmallon should be [MASK]. It allows a 1 card FTK.
   example_title: Banlist Speculation
-- text: "Once per [MASK]: You can toss a coin and call it."
+- text: 'Once per [MASK]: You can toss a coin and call it.'
   example_title: Time Wizard
-- text: "You [MASK] so hard on turn 2."
+- text: You [MASK] so hard on turn 2.
   example_title: Misplay
+library_name: sentence-transformers
 ---
 
-# YGOMiniLM
+# **YGOMiniLM**
 ![time_wiz](https://ms.yugipedia.com//thumb/7/76/TimeWizard-MRD-EN-UR-UE-25thAnniversaryEdition.png/300px-TimeWizard-MRD-EN-UR-UE-25thAnniversaryEdition.png)
 [ImgSource](https://yugipedia.com/wiki/Time_Wizard)
 
@@ -19,6 +26,7 @@ This is a sentence-transformers/paraphrase-MiniLM-L3-v2 model that has undergone
 
 Its intended use is to create sentence embeddings for fast vector search in the domain of YuGiOh discourse.
 
+## **Training Data**
 The training data was split into two parts:
 1) A private collection of data collected from YouTube Comments:
 
@@ -29,4 +37,21 @@ The training data was split into two parts:
 |MSTTV |5340|
 |mkohl40|5224|
 
-2) The Full Database of YuGiOh cards accessed via the [YGOProDeck API](https://ygoprodeck.com/api-guide/) as of 17/05/2023. The `name`, `type`, `race` and `desc` fields were concatenated and delimited by `\t` to create the training examples.
+2) The Full Database of YuGiOh cards accessed via the [YGOProDeck API](https://ygoprodeck.com/api-guide/) as of 17/05/2023. The `name`, `type`, `race` and `desc` fields were concatenated and delimited by `\t` to create the training examples.
+
+## **Usage**
+```
+pip install sentence-transformers
+```
+Then to get embeddings you simply run the following:
+```
+from sentence_transformers import SentenceTransformer
+sentences = ["FLIP: Target 1 monster on the field; destroy that target.",
+              "Union Carrier needs to go.",
+              "Scythe lock is healthy for the game"
+              ]
+
+model = SentenceTransformer("jkswin/YGO_MiniLM")
+embeddings = model.encode(sentences)
+print(embeddings)
+```