up readme roberta

README.md

@@ -37,7 +37,7 @@ model-index:
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This model is a fine-tuned version of sentiment classification 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 **roberta-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:** [j-hartmann/sentiment-roberta-large-english-3-classes](https://huggingface.co/j-hartmann/sentiment-roberta-large-english-3-classes)
+- **Finetuned from model:** [roberta-base](roberta-base)
 
 
 ## Uses
@@ -63,7 +63,7 @@ result = cls_task(sequence)
                                         
 ```
 
-Sentiment classification in multilingual data. Fine-tuned on a balanced corpus of size 8.4k, partially semi-annotated. 
+Sentiment classification in multilingual data. Fine-tuned on a balanced corpus of size 6.2k, partially semi-annotated. 
 Model suited for classification of stance in short text. 
 *Suitable for fine-tuning on hate/offensive language detection.
 
@@ -84,7 +84,7 @@ Normalization of user mentions and hyperlinks to "user" and "url" tokens, respec
 ### Training Hyperparameters
 
 - trained for 2 epochs, mini-batch size of 8.
-- loss: 0.574
+- loss: 0.556
 - learning_rate: 4e-5; weight_decay: 1e-2
 
 ## Evaluation
@@ -93,21 +93,21 @@ Normalization of user mentions and hyperlinks to "user" and "url" tokens, respec
 
 - evaluation on 15% of data.
 
-- accuracy: 91.2
+- accuracy: 0.812
 - macro avg:
-  - f1: 91.4
-  - precision: 91.4
-  - recall: 91.5
+  - f1: 0.816
+  - precision: 0.814
+  - recall: 0.818
 - weighted avg:
-  - f1: 91.2
-  - precision: 91.3
-  - recall: 91.2
+  - f1: 0.812
+  - precision: 0.814
+  - recall: 0.812
 
                        precision    recall  f1-score   support
 
-           neutral       0.930     0.868     0.898       471
-           positive      0.933     0.946     0.940       355
-           negative      0.878     0.931     0.904       433
+           neutral       0.830     0.793     0.811       411
+           positive      0.877     0.881     0.879       243
+           negative      0.736     0.780     0.757       282