Update README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,43 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
|
| 5 |
+
tags:
|
| 6 |
+
- text-classification
|
| 7 |
+
- emotion
|
| 8 |
+
- pytorch
|
| 9 |
+
license: apache-2.0
|
| 10 |
+
datasets:
|
| 11 |
+
- emotion
|
| 12 |
+
metrics:
|
| 13 |
+
- accuracy
|
| 14 |
+
---
|
| 15 |
+
|
| 16 |
+
# bert-base-uncased-emotion
|
| 17 |
+
|
| 18 |
+
## Model description
|
| 19 |
+
|
| 20 |
+
`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.
|
| 21 |
+
|
| 22 |
+
For more details, please see, [the emotion dataset on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotion).
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
#### Limitations and bias
|
| 26 |
+
|
| 27 |
+
- Not the best model, but it works in a pinch I guess...
|
| 28 |
+
- Code not available as I just hacked this together.
|
| 29 |
+
- [Follow me on github](https://github.com/nateraw) to get notified when code is made available.
|
| 30 |
+
|
| 31 |
+
## Training data
|
| 32 |
+
|
| 33 |
+
Data came from HuggingFace's `datasets` package. The data can be viewed [on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotion).
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
## Training procedure
|
| 37 |
+
...
|
| 38 |
+
|
| 39 |
+
## Eval results
|
| 40 |
+
|
| 41 |
+
val_acc - 0.931 (useless, as this should be precision/recall/f1)
|
| 42 |
+
|
| 43 |
+
The score was calculated using PyTorch Lightning metrics.
|