bert-nandha

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: 0.0754
• Train Accuracy: 0.9847
• Validation Loss: 0.0764
• Validation Accuracy: 0.9855
• 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': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.2388	0.9651	0.0972	0.9870	0
0.0842	0.9833	0.0702	0.9861	1
0.0754	0.9847	0.0764	0.9855	2

Framework versions

• Transformers 4.46.2
• TensorFlow 2.16.1
• Datasets 3.0.1
• Tokenizers 0.20.0