mbert-finetuned

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. -->

xmelus/mbert

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

• Train Loss: 1.5424
• Train Accuracy: 0.1446
• Validation Loss: 1.5269
• Validation Accuracy: 0.1461
• Finished epochs: 24

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -596, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, '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: mixed_float16

Training results

Epoch 1/50

loss: 2.9925 - accuracy: 0.1059 - val_loss: 1.9812 - val_accuracy: 0.1331

Epoch 2/50

loss: 1.9979 - accuracy: 0.1307 - val_loss: 1.6063 - val_accuracy: 0.1429

Epoch 3/50

loss: 1.5798 - accuracy: 0.1434 - val_loss: 1.5332 - val_accuracy: 0.1461

Epoch 4/50

loss: 1.5325 - accuracy: 0.1451 - val_loss: 1.5285 - val_accuracy: 0.1458

Epoch 5/50

loss: 1.5415 - accuracy: 0.1448 - val_loss: 1.5449 - val_accuracy: 0.1457

Epoch 6/50

loss: 1.5395 - accuracy: 0.1448 - val_loss: 1.5448 - val_accuracy: 0.1456

Epoch 7/50

loss: 1.5463 - accuracy: 0.1446 - val_loss: 1.5421 - val_accuracy: 0.1454

Epoch 8/50

loss: 1.5352 - accuracy: 0.1451 - val_loss: 1.5536 - val_accuracy: 0.1453

Epoch 9/50

oss: 1.5230 - accuracy: 0.1451 - val_loss: 1.5097 - val_accuracy: 0.1466

Epoch 10/50

loss: 1.5318 - accuracy: 0.1449 - val_loss: 1.5303 - val_accuracy: 0.1460

Epoch 11/50

loss: 1.5364 - accuracy: 0.1448 - val_loss: 1.5280 - val_accuracy: 0.1462

Epoch 12/50

loss: 1.5411 - accuracy: 0.1444 - val_loss: 1.5493 - val_accuracy: 0.1455

Epoch 13/50

loss: 1.5378 - accuracy: 0.1446 - val_loss: 1.5473 - val_accuracy: 0.1456

Epoch 14/50

loss: 1.5357 - accuracy: 0.1449 - val_loss: 1.5310 - val_accuracy: 0.1457

Epoch 15/50

loss: 1.5424 - accuracy: 0.1446 - val_loss: 1.5269 - val_accuracy: 0.1461

Epoch 16/50

loss: 1.5314 - accuracy: 0.1450 - val_loss: 1.5392 - val_accuracy: 0.1456

Epoch 17/50

loss: 1.5309 - accuracy: 0.1451 - val_loss: 1.5567 - val_accuracy: 0.1454

Epoch 18/50

loss: 1.5279 - accuracy: 0.1450 - val_loss: 1.5561 - val_accuracy: 0.1452

Epoch 19/50

loss: 1.5311 - accuracy: 0.1450 - val_loss: 1.5400 - val_accuracy: 0.1460

Epoch 20/50

loss: 1.5332 - accuracy: 0.1449 - val_loss: 1.5347 - val_accuracy: 0.1460

Epoch 21/50

loss: 1.5319 - accuracy: 0.1452 - val_loss: 1.5410 - val_accuracy: 0.1458

Epoch 22/50

loss: 1.5327 - accuracy: 0.1449 - val_loss: 1.5352 - val_accuracy: 0.1460

Epoch 23/50

loss: 1.5278 - accuracy: 0.1451 - val_loss: 1.5289 - val_accuracy: 0.1458

Epoch 24/50

loss: 1.5234 - accuracy: 0.1451 - val_loss: 1.5568 - val_accuracy: 0.1449

Framework versions

• Transformers 4.22.1
• TensorFlow 2.8.2
• Datasets 2.5.1
• Tokenizers 0.12.1