Update README.md

README.md

@@ -6,11 +6,9 @@ tags:
 - sentence-similarity
 ---
 
-# {MODEL_NAME}
+# DistilUSE Podcast Natural Questions
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
+This is a [sentence-transformers](https://www.SBERT.net) model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as [described here](https://www.pinecone.io/learn/spotify-podcast-search/).
 
 ## Usage (Sentence-Transformers)
 
@@ -24,22 +22,12 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["podcast about climate change", "how to make money on the internet"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('pinecone/distiluse-podcast-nq')
 embeddings = model.encode(sentences)
-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})
-
-
 ## Training
 The model was trained with the parameters:
 
@@ -87,4 +75,4 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+James Briggs, [How Spotify Uses Semantic Search for Podcasts](https://www.pinecone.io/learn/spotify-podcast-search/), Pinecone