This is a model card copied from original Tensorflow model version:
https://huggingface.co/fimu-docproc-research/mbert-finetunedxmelus/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
- Torch 1.13.1
- Datasets 2.5.1
- Tokenizers 0.12.1
