NERmodel – AI Model by PrabalAryal | AlphaNeural AI

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PrabalAryal/
NERmodel

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token-classification

generated_from_keras_callback

google-bert/bert-base-multilingual-cased

autotrain_compatible

endpoints_compatible

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NERmodel

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

Epoch: 4

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 46000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Validation Loss	Epoch
0.0107	0.0009	0
0.0022	0.0000	1
0.0004	0.0000	2
0.0008	0.0000	3
0.0001	0.0000	4

Framework versions

Transformers 4.52.4
TensorFlow 2.18.0
Datasets 3.6.0
Tokenizers 0.21.2