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

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Ermira/
bert-base-multilingual-uncased-finetuned-ner

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token-classification

generated_from_keras_callback

google-bert/bert-base-multilingual-uncased

autotrain_compatible

endpoints_compatible

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Ermira/bert-base-multilingual-uncased-finetuned-ner

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

Train Loss: 0.0284
Validation Loss: 0.0543
Train Precision: 0.9354
Train Recall: 0.9459
Train F1: 0.9406
Train Accuracy: 0.9860
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
0.1485	0.0642	0.9083	0.9288	0.9184	0.9820	0
0.0482	0.0561	0.9270	0.9424	0.9347	0.9846	1
0.0284	0.0543	0.9354	0.9459	0.9406	0.9860	2

Framework versions

Transformers 4.41.1
TensorFlow 2.15.0
Datasets 2.19.1
Tokenizers 0.19.1