
# bert-base-uncased-emotion

## Model description

`bert-base-uncased` finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.

For more details, please see, [the emotion dataset on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotion).


#### Limitations and bias

- Not the best model, but it works in a pinch I guess...
- Code not available as I just hacked this together.
- [Follow me on github](https://github.com/nateraw) to get notified when code is made available.

## Training data

Data came from HuggingFace's `datasets` package. The data can be viewed [on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotion).


## Training procedure
...

## Eval results

val_acc - 0.931 (useless, as this should be precision/recall/f1)

The score was calculated using PyTorch Lightning metrics.

{"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion"], "metrics": ["accuracy"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"}
transformers pytorch jax bert text-classification emotion en dataset:emotion license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #bert #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# bert-base-uncased-emotion

## Model description

'bert-base-uncased' finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.

For more details, please see, the emotion dataset on nlp viewer.


#### Limitations and bias

- Not the best model, but it works in a pinch I guess...
- Code not available as I just hacked this together.
- Follow me on github to get notified when code is made available.

## Training data

Data came from HuggingFace's 'datasets' package. The data can be viewed on nlp viewer.


## Training procedure
...

## Eval results

val_acc - 0.931 (useless, as this should be precision/recall/f1)

The score was calculated using PyTorch Lightning metrics.

# bert-base-uncased-emotion ## Model description

'bert-base-uncased' finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.

For more details, please see, the emotion dataset on nlp viewer. #### Limitations and bias

- Not the best model, but it works in a pinch I guess...
- Code not available as I just hacked this together.
- Follow me on github to get notified when code is made available. ## Training data

Data came from HuggingFace's 'datasets' package. The data can be viewed on nlp viewer. ## Training procedure
... ## Eval results

val_acc - 0.931 (useless, as this should be precision/recall/f1)

The score was calculated using PyTorch Lightning metrics.
TAGS
#transformers #pytorch #jax #bert #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # bert-base-uncased-emotion ## Model description

'bert-base-uncased' finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.

For more details, please see, the emotion dataset on nlp viewer. #### Limitations and bias

- Not the best model, but it works in a pinch I guess...
- Code not available as I just hacked this together.
- Follow me on github to get notified when code is made available. ## Training data

Data came from HuggingFace's 'datasets' package. The data can be viewed on nlp viewer. ## Training procedure
... ## Eval results

val_acc - 0.931 (useless, as this should be precision/recall/f1)

The score was calculated using PyTorch Lightning metrics.
passage: TAGS
#transformers #pytorch #jax #bert #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# bert-base-uncased-emotion## Model description

'bert-base-uncased' finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.

For more details, please see, the emotion dataset on nlp viewer.#### Limitations and bias

- Not the best model, but it works in a pinch I guess...
- Code not available as I just hacked this together.
- Follow me on github to get notified when code is made available.## Training data

Data came from HuggingFace's 'datasets' package. The data can be viewed on nlp viewer.## Training procedure
...## Eval results

val_acc - 0.931 (useless, as this should be precision/recall/f1)

The score was calculated using PyTorch Lightning metrics.