jborras18/qa_bert_catalan

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

• Train Loss: 1.4159
• Train End Logits Accuracy: 0.6381
• Train Start Logits Accuracy: 0.5826
• Validation Loss: 1.5331
• Validation End Logits Accuracy: 0.6169
• Validation Start Logits Accuracy: 0.5583
• 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': '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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2140, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train End Logits Accuracy	Train Start Logits Accuracy	Validation Loss	Validation End Logits Accuracy	Validation Start Logits Accuracy	Epoch
2.3671	0.4386	0.3832	1.6448	0.5845	0.5326	0
1.5667	0.6029	0.5472	1.5331	0.6169	0.5583	1
1.4159	0.6381	0.5826	1.5331	0.6169	0.5583	2

Framework versions

• Transformers 4.26.1
• TensorFlow 2.11.0
• Datasets 2.10.0
• Tokenizers 0.13.2