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NER2.0.1-dataset – AI Model by sxandie | AlphaNeural AI
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sxandie
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NER2.0.1-dataset
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transformers
tf
tensorboard
bert
token-classification
generated_from_keras_callback
mit
autotrain_compatible
endpoints_compatible
us
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sxandie/NER2.0.1-dataset
This model is a fine-tuned version of
deepset/gbert-base
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.0892
Validation Loss: 0.1364
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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35640, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
training_precision: mixed_float16
Training results
Train Loss
Validation Loss
Epoch
0.2725
0.1879
0
0.1604
0.1563
1
0.1234
0.1496
2
0.1015
0.1404
3
0.0892
0.1364
4
Framework versions
Transformers 4.30.2
TensorFlow 2.12.0
Datasets 2.2.2
Tokenizers 0.13.3
