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bert_ner_tf_pln – AI Model by vladjr | AlphaNeural AI
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vladjr
/
bert_ner_tf_pln
like
0
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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vladjr/bert_ner_tf_pln
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: 0.1067
Validation Loss: 0.1824
Train Precision: 0.8287
Train Recall: 0.8193
Train F1: 0.8240
Train Accuracy: 0.9458
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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4674, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32
Training results
Train Loss
Validation Loss
Train Precision
Train Recall
Train F1
Train Accuracy
Epoch
0.1890
0.1811
0.8290
0.8053
0.8170
0.9445
0
0.1250
0.1739
0.8243
0.8188
0.8216
0.9457
1
0.1067
0.1824
0.8287
0.8193
0.8240
0.9458
2
Framework versions
Transformers 4.35.2
TensorFlow 2.14.0
Datasets 2.15.0
Tokenizers 0.15.0
