
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.

## Results
Best Result of `Macro F1` - 70%

## Tutorial Link
- [GitHub](https://github.com/justin871030/GoEmotions)
{"language": "en", "license": "mit", "tags": ["go-emotion", "text-classification", "pytorch"], "datasets": ["go_emotions"], "metrics": ["f1"], "widget": [{"text": "Thanks for giving advice to the people who need it! \ud83d\udc4c\ud83d\ude4f"}]}
transformers pytorch bert go-emotion text-classification en dataset:go_emotions license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us 


## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.

## Results
Best Result of 'Macro F1' - 70%

## Tutorial Link
- GitHub
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly. ## Results
Best Result of 'Macro F1' - 70% ## Tutorial Link
- GitHub
TAGS
#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us 
 ## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly. ## Results
Best Result of 'Macro F1' - 70% ## Tutorial Link
- GitHub
passage: TAGS
#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us 
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.## Results
Best Result of 'Macro F1' - 70%## Tutorial Link
- GitHub