added id2label schema

README.md

@@ -11,7 +11,7 @@ language:
 
 # gilleti/emotional-classification
 
-This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for classification of emotions in Swedish text. The model supports multi-label 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 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.
@@ -21,6 +21,18 @@ Accuracy on a number of experiments can be found in the figure below.
 !<img src="./emotional_accuracy.svg">
 ![plot](./emotional_accuracy.svg)
 
+Id to label schema is as follows:
+
+```
+0 : absence of emotion
+1 : happiness (glädje)
+2 : love/empathy (kärlek/empati)
+3: fear/anxiety (oro/rädsla)
+4: sadness/disappointment (sorg/besvikelse)
+5: anger/hate (ilska/hat)
+6: hope/anticipation (hopp/förväntan)
+```
+
 ## The data
 The model has been trained on news headlines that have been manually annotated at KBLab by the PhD student Nora Hansson Bittár.