vnktrmnb/MBERT_FT-TyDiQA_S611

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

• Train Loss: 0.6283
• Train End Logits Accuracy: 0.8362
• Train Start Logits Accuracy: 0.8650
• Validation Loss: 0.4745
• Validation End Logits Accuracy: 0.8698
• Validation Start Logits Accuracy: 0.9046
• Epoch: 2

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': 2412, '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	Train End Logits Accuracy	Train Start Logits Accuracy	Validation Loss	Validation End Logits Accuracy	Validation Start Logits Accuracy	Epoch
1.5215	0.6424	0.6758	0.5020	0.8505	0.8892	0
0.8604	0.7868	0.8167	0.4613	0.8673	0.9149	1
0.6283	0.8362	0.8650	0.4745	0.8698	0.9046	2

Framework versions

• Transformers 4.32.1
• TensorFlow 2.12.0
• Datasets 2.14.4
• Tokenizers 0.13.3