bert-base-multilingual-cased-FT-TyDiQA-GoldP_Te – AI Model by vnktrmnb | AlphaNeural AI

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vnktrmnb/
bert-base-multilingual-cased-FT-TyDiQA-GoldP_Te

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question-answering

generated_from_keras_callback

google-bert/bert-base-multilingual-cased

endpoints_compatible

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vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA-GoldP_Te

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: 0.4459
Train End Logits Accuracy: 0.8633
Train Start Logits Accuracy: 0.8986
Validation Loss: 0.4616
Validation End Logits Accuracy: 0.8570
Validation Start Logits Accuracy: 0.9072
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': 1359, '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
0.4434	0.8660	0.9037	0.4616	0.8570	0.9072	0
0.4507	0.8620	0.9018	0.4616	0.8570	0.9072	1
0.4459	0.8633	0.8986	0.4616	0.8570	0.9072	2

Framework versions

Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.4
Tokenizers 0.13.3