ratish/DBERT_ZS_CleanCollision_v1.1

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0709
• Validation Loss: 1.5936
• Train Accuracy: 0.5862
• Epoch: 9

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Epoch
0.9871	1.0630	0.3448	0
0.8663	1.0509	0.4138	1
0.6717	0.8617	0.5862	2
0.4366	0.8978	0.6897	3
0.2911	0.6636	0.7241	4
0.2351	1.0674	0.6897	5
0.1412	1.1587	0.6552	6
0.0980	1.3062	0.6207	7
0.0748	1.2605	0.6552	8
0.0709	1.5936	0.5862	9

Framework versions

• Transformers 4.28.1
• TensorFlow 2.12.0
• Datasets 2.11.0
• Tokenizers 0.13.3