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bert-base-multilingual-cased-finetuned-TyDiQA_AUQ – AI Model by vnktrmnb | AlphaNeural AI
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vnktrmnb
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bert-base-multilingual-cased-finetuned-TyDiQA_AUQ
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transformers
tf
tensorboard
bert
question-answering
generated_from_keras_callback
google-bert/bert-base-multilingual-cased
finetune
apache-2.0
endpoints_compatible
us
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vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA_AUQ
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: 1.2483
Train End Logits Accuracy: 0.6727
Train Start Logits Accuracy: 0.7141
Validation Loss: 0.5474
Validation End Logits Accuracy: 0.8273
Validation Start Logits Accuracy: 0.8802
Epoch: 0
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': 813, '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
1.2483
0.6727
0.7141
0.5474
0.8273
0.8802
0
Framework versions
Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.1
Tokenizers 0.13.3
