Update README.md

README.md

@@ -7,8 +7,25 @@ tags:
 pipeline_tag: text-classification
 ---
 
+
+
 # multi-label_8_sent_v3_1
 
+This experimental multi-label model has been finetuned on NSS data to classify comments into 8 topic classes:
+
+1. Teaching & Learning
+2. Support
+3. Communication
+4. Organisation and timetable
+5. Assessment and feedback
+6. Career and placement
+7. Health, wellbeing and social life
+8. Facilities and technology
+
+Inference works best at sentence-level.
+
+# SetFit
+
 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.
@@ -30,7 +47,7 @@ from setfit import SetFitModel
 # Download from Hub and run inference
 model = SetFitModel.from_pretrained("multi-label_8_sent_v3_1")
 # Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+preds = model(["My lecturers were excellent!", "I wish we'd had more support when it came to assignments."])
 ```
 
 ## BibTeX entry and citation info