added detail to model card

README.md

@@ -5,11 +5,12 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
+- question-answering
 ---
 
-# {MODEL_NAME}
+# MPNet Retriever (Discourse)
 
-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.
+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 as a retriever model in open-domain question-answering tasks.
 
 <!--- Describe your model here -->
 
@@ -70,16 +71,10 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
+## Training
 
+The model was fine-tuned on question-answer pairs scraper from several ML-focused Discourse forums \[HuggingFace, PyTorch, Streamlit, TensorFlow\].
 
-## 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})
-
-
-## Training
 The model was trained with the parameters:
 
 **DataLoader**:
@@ -125,4 +120,4 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+Fine-tuned by [James Briggs](https://www.youtube.com/c/jamesbriggs) at [Pinecone](https://www.pinecone.io). Learn more about the [fine-tuning process here](https://www.pinecone.io/learn/retriever-models/).