Update README.md

README.md

@@ -11,10 +11,11 @@ pipeline_tag: text-classification
 
 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
 
-1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning ("sentence-transformers/paraphrase-mpnet-base-v2" specifically in this case).
 2. Training a classification head with features from the fine-tuned Sentence Transformer.
 
-This model was finetuned with the dataset `joshuapsa/gpt-generated-news-sentences`. Please refer to this to understand the label meanings.
+This model was finetuned with the custom dataset `joshuapsa/gpt-generated-news-sentences`, which is a synthetic dataset containing news sentences and their topics.<br> 
+Please refer to this to understand the label meanings of the prediction output.
 
 ## Usage
 
@@ -30,10 +31,12 @@ You can then run inference as follows:
 from setfit import SetFitModel
 
 # Download from Hub and run inference
-model = SetFitModel.from_pretrained("joshuapsa/setfit-ai-generated-sent")
+model = SetFitModel.from_pretrained("joshuapsa/setfit-news-topic-sentences")
 # Run inference
 preds = model(["Tensions escalated in the Taiwan Strait as Chinese and Taiwanese naval vessels engaged in a standoff, raising fears of a potential conflict.",\
  "Following the highway closure in Toronto, transportation officials announce plans for the construction of additional lanes and improved traffic management systems."])
+# The underlying model body of the setfit model is a SentenceTransformer model, hence you can use it to encode a raw sentence into dense embeddings:
+emb = model.model_body.encode("Your sentence goes here")
 ```
 
 ## BibTeX entry and citation info