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ibrahimVicomtech/
baseline_model

Name: baseline_model
Rating: 2.5 (6 reviews)

transformers

distilbert

token-classification

generated_from_keras_callback

distilbert/distilbert-base-uncased

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baseline_model

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

Train Loss: 1.3537
Validation Loss: 1.1238
Train Precision: 0.4228
Train Recall: 0.4093
Train F1: 0.4160
Train Accuracy: 0.7712
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': 216, '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
2.8910	1.8205	0.4147	0.0451	0.0813	0.6325	0
1.6360	1.3609	0.3788	0.3566	0.3673	0.7525	1
1.3537	1.1238	0.4228	0.4093	0.4160	0.7712	2

Framework versions

Transformers 4.41.2
TensorFlow 2.16.1
Datasets 2.20.0
Tokenizers 0.19.1

ibrahimVicomtech/baseline_model

baseline_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ibrahimVicomtech/
baseline_model