HWJin/SMU-NLP-assignment2-finetuned-best

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

• Train Loss: 0.9936
• Validation Loss: 0.9867
• Epoch: 13

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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 990, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 10, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: mixed_float16

Training results

Train Loss	Validation Loss	Epoch
1.6490	1.2199	0
1.2679	1.1622	1
1.1796	1.0931	2
1.1200	1.0274	3
1.0841	1.0739	4
1.0567	1.0317	5
1.0164	0.9895	6
0.9819	1.0365	7
0.9960	0.9857	8
1.0143	0.9954	9
1.0156	1.0173	10
0.9915	1.0391	11
1.0246	1.0288	12
0.9936	0.9867	13

Framework versions

• Transformers 4.19.2
• TensorFlow 2.8.2
• Datasets 2.2.2
• Tokenizers 0.12.1