Update README.md

README.md

@@ -9,7 +9,7 @@ tags:
 
 # kuelumbus/polyBERT
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 600 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics. polyBERT maps PSMILES strings to 600 dimensional dense fingerprints. The fingerprints numerically represent polymer chemical structures.
 
 <!--- Describe your model here -->
 
@@ -25,10 +25,10 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
 
 model = SentenceTransformer('kuelumbus/polyBERT')
-embeddings = model.encode(sentences)
+embeddings = model.encode(psmiles_strings)
 print(embeddings)
 ```
 
@@ -50,35 +50,31 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('kuelumbus/polyBERT')
 model = AutoModel.from_pretrained('kuelumbus/polyBERT')
 
 # Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+encoded_input = tokenizer(psmiles_strings, padding=True, truncation=True, return_tensors='pt')
 
 # Compute token embeddings
 with torch.no_grad():
     model_output = model(**encoded_input)
 
 # Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+fingerprints = mean_pooling(model_output, encoded_input['attention_mask'])
 
-print("Sentence embeddings:")
-print(sentence_embeddings)
+print("Fingerprints:")
+print(fingerprints)
 ```
 
 
 
 ## 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=kuelumbus/polyBERT)
-
-
+See https://github.com/Ramprasad-Group/polyBERT
 
 ## Full Model Architecture
 ```
@@ -90,4 +86,4 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+t.b.d.