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--- |
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language: |
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- en |
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thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 |
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tags: |
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- text-classification |
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- emotion |
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- pytorch |
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license: apache-2.0 |
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datasets: |
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- emotion |
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metrics: |
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- accuracy |
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--- |
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# bert-base-uncased-emotion |
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## Model description |
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`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. |
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For more details, please see, [the emotion dataset on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotion). |
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#### Limitations and bias |
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- Not the best model, but it works in a pinch I guess... |
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- Code not available as I just hacked this together. |
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- [Follow me on github](https://github.com/nateraw) to get notified when code is made available. |
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## Training data |
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Data came from HuggingFace's `datasets` package. The data can be viewed [on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotion). |
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## Training procedure |
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... |
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## Eval results |
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val_acc - 0.931 (useless, as this should be precision/recall/f1) |
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The score was calculated using PyTorch Lightning metrics. |
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