xlm-roberta-base-FT-TyDiQA_SYNAUQ – AI Model by vnktrmnb | AlphaNeural AI

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vnktrmnb/
xlm-roberta-base-FT-TyDiQA_SYNAUQ

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vnktrmnb/xlm-roberta-base-FT-TyDiQA_SYNAUQ

This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.4265
Train End Logits Accuracy: 0.8515
Train Start Logits Accuracy: 0.9052
Validation Loss: 0.4948
Validation End Logits Accuracy: 0.8448
Validation Start Logits Accuracy: 0.9049
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': 4086, '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
1.5062	0.6059	0.6533	0.5270	0.8308	0.8867	0
0.6589	0.7895	0.8564	0.4835	0.8406	0.8979	1
0.4265	0.8515	0.9052	0.4948	0.8448	0.9049	2

Framework versions

Transformers 4.32.0
TensorFlow 2.12.0
Datasets 2.14.4
Tokenizers 0.13.3