Update .ipynb_checkpoints/README-checkpoint.md

.ipynb_checkpoints/README-checkpoint.md

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+---
+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.