README.md

metadata

license: apache-2.0
base_model: PlanTL-GOB-ES/roberta-base-bne
tags:
  - generated_from_keras_callback
model-index:
  - name: lulygavri/rob-conv
    results: []

lulygavri/rob-conv

This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0787
• Validation Loss: 0.0220
• Train Accuracy: 0.9948
• Train Precision: [0.95822589 0.99925584 0.99829758]
• Train Precision W: 0.9949
• Train Recall: [0.99678112 0.99385686 0.99761824]
• Train Recall W: 0.9948
• Train F1: [0.97712332 0.99654904 0.99795779]
• Train F1 W: 0.9948
• Epoch: 1

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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3964, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 500, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: mixed_float16

Training results

Train Loss	Validation Loss	Train Accuracy	Train Precision	Train Precision W	Train Recall	Train Recall W	Train F1	Train F1 W	Epoch
0.0787	0.0220	0.9948	[0.95822589 0.99925584 0.99829758]	0.9949	[0.99678112 0.99385686 0.99761824]	0.9948	[0.97712332 0.99654904 0.99795779]	0.9948	1

Framework versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.16.1
• Tokenizers 0.15.1