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distilbert-base-uncased-finetuned-ner – AI Model by rbonazzola | AlphaNeural AI
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rbonazzola
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distilbert-base-uncased-finetuned-ner
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transformers
tf
tensorboard
distilbert
token-classification
generated_from_keras_callback
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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rbonazzola/distilbert-base-uncased-finetuned-ner
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: 0.0336
Validation Loss: 0.0604
Train Precision: 0.9208
Train Recall: 0.9348
Train F1: 0.9277
Train Accuracy: 0.9831
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': 2631, '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
0.1929
0.0717
0.8951
0.9179
0.9063
0.9789
0
0.0537
0.0613
0.9240
0.9299
0.9269
0.9828
1
0.0336
0.0604
0.9208
0.9348
0.9277
0.9831
2
Framework versions
Transformers 4.44.2
TensorFlow 2.17.0
Datasets 3.0.1
Tokenizers 0.19.1
