Update README.md

README.md

@@ -7,6 +7,8 @@ tags:
 datasets:
 - go_emotions
 license: mit
+widget:
+- text: "I feel lucky to be here."
 ---
 
 # distilbert-base-uncased-go-emotions-student
@@ -15,15 +17,12 @@ license: mit
 
 This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using [this
 script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation).
-It is the result of the demo notebook
-[here](https://colab.research.google.com/drive/1mjBjd0cR8G57ZpsnFCS3ngGyo5nCa9ya?usp=sharing), where more details
-about the model can be found.
-
-- Teacher model: [roberta-large-mnli](https://huggingface.co/roberta-large-mnli)
+It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. 
 
 ## Intended Usage
 
 The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model
 trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
-can be distilled to a more efficient student. Note that although the GoEmotions dataset allow multiple labels
-per instance, the teacher used single-label classification to create psuedo-labels.
+can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note
+that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label 
+classification to create psuedo-labels.