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distilbert-base-uncased-finetuned-ner – AI Model by Cube | AlphaNeural AI
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Cube
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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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Cube/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: 0.0339
Validation Loss: 0.0646
Train Precision: 0.9217
Train Recall: 0.9295
Train F1: 0.9256
Train Accuracy: 0.9827
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
0.1996
0.0735
0.8930
0.9179
0.9053
0.9784
0
0.0545
0.0666
0.9137
0.9292
0.9214
0.9817
1
0.0339
0.0646
0.9217
0.9295
0.9256
0.9827
2
Framework versions
Transformers 4.19.2
TensorFlow 2.8.2
Datasets 2.2.2
Tokenizers 0.12.1
