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distilbert-base-uncased-finetuned-ner – AI Model by mattladewig | AlphaNeural AI
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mattladewig
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distilbert-base-uncased-finetuned-ner
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transformers
tf
tensorboard
distilbert
token-classification
generated_from_keras_callback
apache-2.0
autotrain_compatible
endpoints_compatible
us
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mattladewig/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: 0.0342
Validation Loss: 0.0614
Train Precision: 0.9248
Train Recall: 0.9365
Train F1: 0.9306
Train Accuracy: 0.9833
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': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, '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}
training_precision: float32
Training results
Train Loss
Validation Loss
Train Precision
Train Recall
Train F1
Train Accuracy
Epoch
0.1951
0.0694
0.9087
0.9181
0.9134
0.9799
0
0.0530
0.0621
0.9246
0.9301
0.9273
0.9823
1
0.0342
0.0614
0.9248
0.9365
0.9306
0.9833
2
Framework versions
Transformers 4.30.2
TensorFlow 2.12.0
Datasets 2.13.0
Tokenizers 0.13.3
