nateraw/bert-base-uncased-emotion converted to ONNX and quantized using optimum.

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

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Eval results

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

The score was calculated using PyTorch Lightning metrics.