robbery_dataset_tf_finetuned_20230213_delitos_validados

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

• Train Loss: 0.1837
• Train Sparse Categorical Accuracy: 0.9465
• Validation Loss: 0.3629
• Validation Sparse Categorical Accuracy: 0.8995
• Epoch: 9

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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-06, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Sparse Categorical Accuracy	Validation Loss	Validation Sparse Categorical Accuracy	Epoch
1.2571	0.6335	0.6850	0.7797	0
0.5901	0.8318	0.4557	0.8733	1
0.4303	0.8821	0.3878	0.8888	2
0.3557	0.9000	0.3471	0.9030	3
0.3096	0.9122	0.3476	0.9000	4
0.2750	0.9212	0.3380	0.9005	5
0.2474	0.9295	0.3238	0.9053	6
0.2210	0.9355	0.3464	0.9022	7
0.2016	0.9416	0.3296	0.9053	8
0.1837	0.9465	0.3629	0.8995	9

Framework versions

• Transformers 4.26.1
• TensorFlow 2.11.0
• Datasets 2.9.0
• Tokenizers 0.13.2