up readme distil

README.md

@@ -28,16 +28,16 @@ model-index:
       name: unpublished          # Required. A pretty name for the dataset. Example: Common Voice (French)
     metrics:
       - type: f1          
-        value: 81.6
+        value: 77.8
       - type: accuracy          
-        value: 81.2    
+        value: 78.5   
 ---
 
 # eevvgg/StanceBERTa
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This model is a fine-tuned version of **roberta-base** model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
+This model is a fine-tuned version of **distilroberta-base** model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
 Fine-tuned on a larger and more balanced data sample compared with the previous version [eevvgg/BEtMan-Tw](https://huggingface.co/eevvgg/BEtMan-Tw). 
 
 
@@ -45,7 +45,7 @@ Fine-tuned on a larger and more balanced data sample compared with the previous
 
 - **Model type:** RoBERTa for stance classification
 - **Language(s) (NLP):** English social media data from Twitter and Reddit
-- **Finetuned from model:** [roberta-base](roberta-base)
+- **Finetuned from model:** [distilroberta-base](distilroberta-base)
 
 
 ## Uses
@@ -82,9 +82,9 @@ Normalization of user mentions and hyperlinks to "user" and "url" tokens, respec
 
 ### Training Hyperparameters
 
-- trained for 2 epochs, mini-batch size of 8.
-- loss: 0.556
-- learning_rate: 4e-5; weight_decay: 1e-2
+- trained for 3 epochs, mini-batch size of 8.
+- loss: 0.509
+- learning_rate: 5e-5; weight_decay: 1e-2
 
 ## Evaluation
 
@@ -92,22 +92,15 @@ Normalization of user mentions and hyperlinks to "user" and "url" tokens, respec
 
 - evaluation on 15% of data.
 
-- accuracy: 0.812
+- accuracy: 0.785
 - macro avg:
-  - f1: 0.816
-  - precision: 0.814
-  - recall: 0.818
+  - f1: 0.778
+  - precision: 0.779
+  - recall: 0.778
 - weighted avg:
-  - f1: 0.812
-  - precision: 0.814
-  - recall: 0.812
-
-                       precision    recall  f1-score   support
-
-           neutral       0.830     0.793     0.811       411
-           positive      0.877     0.881     0.879       243
-           negative      0.736     0.780     0.757       282
-
+  - f1: 0.786
+  - precision: 0.786
+  - recall: 0.785
 
 
 ## Citation