language: | |
- en | |
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 | |
tags: | |
- text-classification | |
- emotion | |
- pytorch | |
license: apache-2.0 | |
datasets: | |
- emotion | |
metrics: | |
- accuracy | |
# 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. | |