readme fixes

README.md

@@ -13,8 +13,8 @@ language:
 
 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for classification of emotions in Swedish text. The model supports seven basic emotions, listed below. 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.
+1. Finetuning a [KBLab Sentence Transformer in Swedish](https://huggingface.co/KBLab/sentence-bert-swedish-cased) with contrastive learning.
+2. Training a classification head with features from the finetuned Sentence Transformer.
 
 Accuracy on a number of experiments on a minimal test set (35 examples) can be found in the figure below.
 
@@ -56,7 +56,7 @@ model = SetFitModel.from_pretrained("gilleti/emotional-classification")
 preds = model(["Ingen tech-dystopi slår människans inre mörker", "Ina Lundström: Jag har två Bruce-tatueringar"])
 ```
 
-This outputs predictions sadness/disappointment and absence of emotion. Please note that these examples are cherrypicked as most headlines (which is what the model is trained on) are rarely clear.
+This outputs predictions sadness/disappointment and absence of emotion. Keep in mind that these examples are cherrypicked as most headlines (which is what the model is trained on) are rarely as clear.
 
 ## BibTeX entry and citation info