Update README.md

README.md

@@ -9,7 +9,9 @@ pipeline_tag: text-classification
 
 # /private/var/folders/6b/0g07c1bd5nx_dqlnklk5kq5h0000gn/T/tmpn6ptrcp2/JohanHeinsen/PE_header_classifier
 
-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:
+This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. It was created to identify headers in the publication Politiets Efterretninger (1867–1890)
+
+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.
@@ -30,9 +32,13 @@ from setfit import SetFitModel
 # Download from Hub and run inference
 model = SetFitModel.from_pretrained("/private/var/folders/6b/0g07c1bd5nx_dqlnklk5kq5h0000gn/T/tmpn6ptrcp2/JohanHeinsen/PE_header_classifier")
 # Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+preds = model(["VI. Andre meddelelser", "1) Reserven er løbet bort."])
 ```
 
+## Metrics: 
+Accuracy: 0.9977494373593399 
+F1: 0.953125
+
 ## BibTeX entry and citation info
 
 ```bibtex