Update README.md

README.md

@@ -9,57 +9,26 @@ datasets:
 license: mit
 ---
 
-Connect me on LinkedIn
-- [linkedin.com/in/arpanghoshal](https://www.linkedin.com/in/arpanghoshal)
+## What is the GoEmotions Dataset?
 
+The dataset is comprised of 58000 Reddit comments with 28 emotions.
 
-## What is GoEmotions
-
-Dataset labelled 58000 Reddit comments with 28 emotions
-
-- admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise + neutral
-
-
-## What is RoBERTa
-
-RoBERTa builds on BERT’s language masking strategy and modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. RoBERTa was also trained on an order of magnitude more data than BERT, for a longer amount of time. This allows RoBERTa representations to generalize even better to downstream tasks compared to BERT.
-
-
-## Hyperparameters
-
-| Parameter         |      |
-| ----------------- | :---: |
-| Learning rate     | 5e-5 |
-| Epochs            |   10 |
-| Max Seq Length    |   50 |
-| Batch size        |   16 |
-| Warmup Proportion | 0.1 |
-| Epsilon      | 1e-8 |
-
-
-## Results
-
-Best Result of `Macro F1` - 49.30%
-
+- admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise
 ## Usage
 
 ```python
 
 from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline
 
-tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa")
-model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa")
+tokenizer = RobertaTokenizerFast.from_pretrained("cappuch/EmoRoBERTa_Retrain")
+model = TFRobertaForSequenceClassification.from_pretrained("cappuch/EmoRoBERTa_Retrain")
 
 emotion = pipeline('sentiment-analysis', 
-                    model='arpanghoshal/EmoRoBERTa')
+                    model='cappuch/EmoRoBERTa_Retrain')
 
-emotion_labels = emotion("Thanks for using it.")
+emotion_labels = emotion("Hello!")
 print(emotion_labels)
 
-```
-Output 
-
-```
-[{'label': 'gratitude', 'score': 0.9964383244514465}]
+#[{'label': 'neutral', 'score': 0.9964383244514465}]
 ```