Update README.md

README.md

@@ -1,65 +0,0 @@
----
-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}]
-```
-