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- [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}]
```


{"language": "en", "license": "mit", "tags": ["text-classification", "tensorflow", "roberta"], "datasets": ["go_emotions"]}
transformers tf roberta text-classification tensorflow en dataset:go_emotions license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #tf #roberta #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

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* URL


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
---------------



Results
-------


Best Result of 'Macro F1' - 49.30%


Usage
-----


Output



TAGS
#transformers #tf #roberta #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #tf #roberta #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
