Update README.md

README.md

@@ -9,21 +9,10 @@ datasets:
 
 
 # Model Description
+Yet another Transformer model fine-tuned for approximating another non-linear mapping between X and Y? That's right!
+This is your good ol' emotion classifier - given an input text, the model outputs a probability distribution over a set of pre-selected emotion words. In this case, it is 32, which is the number of emotion classes in the [Empathetic Dialogues](https://huggingface.co/datasets/bdotloh/empathetic-dialogues-contexts) dataset.
 
-We performed transfer learning experiments on a distilbert-base-uncased model fine-tuned on the GoEmotions dataset for the purpose of classifying [(emotional) prompts in the Empathetic Dialogues dataset](https://huggingface.co/datasets/bdotloh/empathetic-dialogues-contexts). 
-
-The fine-tuned distilbert-base-uncased can be found [here](https://huggingface.co/bhadresh-savani/bert-base-go-emotion).
-
-### Limitations and bias
-
-GoEmotions:
-1) Demographics of Reddit Users 
-2) Imbalanced class distribution
-3) ...
-
-EmpatheticDialogues: 
-1) Unable to ascertain the degree of cultural specificity for the context that a respondent described when given an emotion label (i.e., p(description | emotion, *culture*))
-2) ...
+This model is built "on top of" a [distilbert-base-uncased model fine-tuned on the go-emotions dataset](https://huggingface.co/bhadresh-savani/bert-base-go-emotion). Y'all should really check out that model, it even contains a jupyter notebook file that illustrates how the model was trained (bhadresh-savani if you see this, thank you!).  
 
 
 ## Training data
@@ -34,3 +23,9 @@ EmpatheticDialogues:
 
 ## Evaluation results
 
+### Limitations and bias
+Well where should we begin...
+
+EmpatheticDialogues: 
+1) Unable to ascertain the degree of cultural specificity for the context that a respondent described when given an emotion label (i.e., p(description | emotion, *culture*))
+2) ...