Update README.md

README.md

@@ -5,7 +5,7 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-industry-bert-contracts-v0.1 is part of a series of industry-fine-tuned sentence_transformer embedding models.   
+industry-bert-loans is part of a series of industry-fine-tuned sentence_transformer embedding models.   
 
 ## Model Details
 
@@ -13,9 +13,8 @@ industry-bert-contracts-v0.1 is part of a series of industry-fine-tuned sentence
 
 <!-- Provide a longer summary of what this model is. -->
 
-industry-bert-contracts-v0.1 is a domain fine-tuned BERT-based 768-parameter Sentence Transformer model, intended to as a "drop-in" 
-substitute for contractual and legal domains.   This model was trained on a wide range of publicly available commercial contracts, 
-including open source contract datasets. 
+industry-bert-loans is a domain fine-tuned BERT-based 768-parameter Sentence Transformer model, intended to as a "drop-in" 
+substitute optimized for loan agreements.   This model was trained on a wide range of publicly available commercial lending agreements.  
 
 - **Developed by:** llmware
 - **Model type:** BERT-based Industry domain fine-tuned Sentence Transformer architecture
@@ -27,13 +26,13 @@ including open source contract datasets.
 
 from transformers import AutoTokenizer, AutoModel
 
-tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-contracts-v0.1")
-model = AutoModel.from_pretrained("llmware/industry-bert-contracts-v0.1")
+tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-loan")
+model = AutoModel.from_pretrained("llmware/industry-bert-loan")
 
 
 ## Bias, Risks, and Limitations
 
-This is a semantic embedding model, fine-tuned on public domain contracts and related documents.   Results may vary if used outside of this
+This is a semantic embedding model, fine-tuned on publicly available loan, security, credit and underwriting agreements.   Results may vary if used outside of this
 domain, and like any embedding model, there is always the potential for anomalies in the vector embedding space.   No specific safeguards have
 put in place for safety or mitigate potential bias in the dataset.