Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+tags:
+- text-classification
+- tensorflow
+- roberta
+datasets:
+- go_emotions
+license: mit
+---
+
+Contributors:
+- Rohan Kamath [linkedin.com/in/rohanrkamath](https://www.linkedin.com/in/rohanrkamath/)
+- Arpan Ghoshal [linkedin.com/in/arpanghoshal](https://www.linkedin.com/in/arpanghoshal)
+
+## 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%
+
+## Usage
+
+```python
+
+from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline
+
+tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa")
+model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa")
+
+emotion = pipeline('sentiment-analysis', 
+                    model='arpanghoshal/EmoRoBERTa')
+
+emotion_labels = emotion("Thanks for using it.")
+print(emotion_labels)
+
+```
+Output 
+
+```
+[{'label': 'gratitude', 'score': 0.9964383244514465}]
+```
+