Update README.md

README.md

@@ -9,10 +9,7 @@ pipeline_tag: text-classification
 
 # librarian-bots/is_new_dataset_student_model
 
-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.
-2. Training a classification head with features from the fine-tuned Sentence Transformer.
+This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model is trained to predict whether a title + abstract for a paper on arXiv introduces a new dataset. 
 
 ## Usage
 
@@ -30,11 +27,23 @@ from setfit import SetFitModel
 # Download from Hub and run inference
 model = SetFitModel.from_pretrained("librarian-bots/is_new_dataset_student_model")
 # Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+preds = model([Abstract + Title])
+```
+
+During model training, the text was formatted using the following format: 
+
 ```
+TITLE: title text
+ABSTRACT: abstract text
+```
+
+You probably want to use the same format when running inference for this model.  
+
 
 ## BibTeX entry and citation info
 
+To cite the SetFit approach used to train this model please use this citation:
+
 ```bibtex
 @article{https://doi.org/10.48550/arxiv.2209.11055,
 doi = {10.48550/ARXIV.2209.11055},