MBERT_FT-TyDiQA_S41 – AI Model by vnktrmnb | AlphaNeural AI

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vnktrmnb/
MBERT_FT-TyDiQA_S41

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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_S41

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: 0.6256
Train End Logits Accuracy: 0.8359
Train Start Logits Accuracy: 0.8649
Validation Loss: 0.4800
Validation End Logits Accuracy: 0.8595
Validation Start Logits Accuracy: 0.8995
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
1.4994	0.6497	0.6777	0.4953	0.8479	0.8982	0
0.8529	0.7875	0.8176	0.4775	0.8544	0.8892	1
0.6256	0.8359	0.8649	0.4800	0.8595	0.8995	2

Framework versions

Transformers 4.32.1
TensorFlow 2.12.0
Datasets 2.14.4
Tokenizers 0.13.3