Update README.md

README.md

@@ -73,6 +73,13 @@ model-index:
 
 This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
+1. bert-base-uncased was pretrained on a large corpus of open access philosophy text.
+2. This model was further trained using TSDAE on a subset of sentences from this corpus for 6 epochs. 
+3. Resulting model was finetuned using cosine similarity objective on the "philsim" private dataset.
+4. Resulting model was finetuned using cosine similarity objective on the beatai-philosophy dataset.
+
+Model internal name: pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-20e
+
 ## Model Details
 
 ### Model Description
@@ -115,7 +122,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-20e")
+model = SentenceTransformer("dbourget/philai-embeddings-2.0")
 # Run inference
 sentences = [
     'scientific revolutions',