MBERT_FT-TyDiQA_S611 – AI Model by vnktrmnb | AlphaNeural AI

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vnktrmnb/
MBERT_FT-TyDiQA_S611

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question-answering

generated_from_keras_callback

google-bert/bert-base-multilingual-cased

endpoints_compatible

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vnktrmnb/MBERT_FT-TyDiQA_S611

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: 0.6283
Train End Logits Accuracy: 0.8362
Train Start Logits Accuracy: 0.8650
Validation Loss: 0.4745
Validation End Logits Accuracy: 0.8698
Validation Start Logits Accuracy: 0.9046
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': 2412, '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
1.5215	0.6424	0.6758	0.5020	0.8505	0.8892	0
0.8604	0.7868	0.8167	0.4613	0.8673	0.9149	1
0.6283	0.8362	0.8650	0.4745	0.8698	0.9046	2

Framework versions

Transformers 4.32.1
TensorFlow 2.12.0
Datasets 2.14.4
Tokenizers 0.13.3