updated read me

README.md

@@ -6,11 +6,12 @@ tags:
 - sentence-similarity
 ---
 
-# {MODEL_NAME}
+# Manager for Intelligent Knowledge Access (MIKA)
+# Custom Information Retrieval Model
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-<!--- Describe your model here -->
+The model is custom trained on engineering documents for asymmetric infromation retrieval. It is intended to be used to identify engineering documents relevant to a query for use in design time. For example, a repository can be queried to find support for requirements or learn more about a specific type of failure.
 
 ## Usage (Sentence-Transformers)
 
@@ -35,10 +36,12 @@ print(embeddings)
 
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+This model was evaluated on three queries using precision at k for k=10,20, and 30. Mean average precision (MAP) was also calculated. The model was baselines against the pre-trained SBERT.
 
+|IR Method | MAP |
+|----------|-----|
+|Pre-trained sBERT| 0.648|
+|Fine-tuned sBERT| 0.807|
 
 ## Training
 The model was trained with the parameters:
@@ -87,4 +90,4 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+Walsh, HS, & Andrade, SR. "Semantic Search With Sentence-BERT for Design Information Retrieval." Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 42nd Computers and Information in Engineering Conference (CIE). St. Louis, Missouri, USA. August 14–17, 2022. V002T02A066. ASME. https://doi.org/10.1115/DETC2022-89557