bert-base-uncased-finetuned-ner – AI Model by francheutsia | AlphaNeural AI

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francheutsia/
bert-base-uncased-finetuned-ner

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token-classification

generated_from_keras_callback

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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francheutsia/bert-base-uncased-finetuned-ner

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.0229
Validation Loss: 0.0465
Train Precision: 0.8265
Train Recall: 0.8702
Train F1: 0.8478
Train Accuracy: 0.9850
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': 1602, '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
0.1034	0.0641	0.6823	0.8230	0.7461	0.9751	0
0.0419	0.0433	0.8160	0.8499	0.8326	0.9836	1
0.0229	0.0465	0.8265	0.8702	0.8478	0.9850	2

Framework versions

Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.4
Tokenizers 0.13.3