# Knowledge Continuity Regularized Network
Dataset: ANLI
Round: None

Trainer Hyperparameters:
- `lr` = 5e-05
- `per_device_batch_size` = 8
- `gradient_accumulation_steps` = 2
- `weight_decay` = 1e-09
- `seed` = 42

Regularization Hyperparameters
- `numerical stability denominator constant` = 1.0
- `lambda` = 1.0
- `alpha` = 1.0
- `beta` = 1.0

Extended Logs:

|eval_loss|eval_accuracy|epoch|
|--|--|--|
|1.083|0.438|1.0|
|1.077|0.446|2.0|
|1.082|0.443|3.0|
|1.081|0.450|4.0|
|1.082|0.443|5.0|
|1.083|0.447|6.0|
|1.076|0.458|7.0|
|1.088|0.446|8.0|
|1.096|0.434|9.0|
|1.086|0.441|10.0|
|1.083|0.457|11.0|
|1.088|0.445|12.0|
|1.085|0.448|13.0|
|1.085|0.447|14.0|

**Test Accuracy: 0.446**
{"language": "en", "license": "mit", "library_name": "pytorch"}
pytorch en license:mit region:us