You can deploy this model and start earning money today!

MarcoLYH/
bert-base-uncased-finetuned-v2

Name: bert-base-uncased-finetuned-v2
Availability: InStock

generated_from_keras_callback

mit

endpoints_compatible

Views

No views yet

MarcoLYH/bert-base-uncased-finetuned-v2

This model is a fine-tuned version of csarron/bert-base-uncased-squad-v1 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.6988
Train End Logits Accuracy: 0.8958
Train Start Logits Accuracy: 0.7917
Validation Loss: 0.5760
Validation End Logits Accuracy: 0.8500
Validation Start Logits Accuracy: 0.8500
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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 27, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 3, 'power': 1.0, '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
1.9477	0.4375	0.5417	1.3534	0.6000	0.7000	0
1.7097	0.4583	0.5208	1.0429	0.7000	0.7000	1
1.3797	0.6875	0.5833	0.8816	0.75	0.7000	2
1.1394	0.625	0.6875	0.7849	0.75	0.7000	3
1.0558	0.6875	0.625	0.7057	0.8000	0.7000	4
0.8213	0.8542	0.8125	0.6496	0.8500	0.75	5
0.8661	0.8125	0.7292	0.6152	0.8500	0.8000	6
0.7864	0.8542	0.7917	0.5948	0.8500	0.8500	7
0.8933	0.8333	0.75	0.5822	0.8500	0.8500	8
0.6988	0.8958	0.7917	0.5760	0.8500	0.8500	9

Framework versions

Transformers 4.30.1
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3