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distilbert-base-uncased-finetuned-ner – AI Model by sigaldanilov | AlphaNeural AI
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sigaldanilov
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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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sigaldanilov/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: 0.0350
Validation Loss: 0.0595
Train Precision: 0.9219
Train Recall: 0.9348
Train F1: 0.9283
Train Accuracy: 0.9835
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
0.2003
0.0751
0.8887
0.9168
0.9025
0.9784
0
0.0550
0.0603
0.9136
0.9350
0.9242
0.9826
1
0.0350
0.0595
0.9219
0.9348
0.9283
0.9835
2
Framework versions
Transformers 4.42.4
TensorFlow 2.15.0
Datasets 2.20.0
Tokenizers 0.19.1
