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bert-finetuned-ner-4 – AI Model by Gio200023 | AlphaNeural AI
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Gio200023
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bert-finetuned-ner-4
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transformers
tf
distilbert
token-classification
generated_from_keras_callback
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Gio200023/bert-finetuned-ner-4
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.1152
Validation Loss: 0.2524
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': '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': 634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '
passive_serialization
': True}, 'warmup_steps': 2, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
training_precision: float32
Training results
Train Loss
Validation Loss
Epoch
0.3687
0.3069
0
0.1532
0.2579
1
0.1152
0.2524
2
Framework versions
Transformers 4.33.0
TensorFlow 2.12.0
Datasets 2.1.0
Tokenizers 0.13.3
