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

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vnktrmnb/
xlm-roberta-base-FT-TyDiQA_AUQC

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vnktrmnb/xlm-roberta-base-FT-TyDiQA_AUQC

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: 0.7421
Train End Logits Accuracy: 0.8018
Train Start Logits Accuracy: 0.8399
Validation Loss: 0.4881
Validation End Logits Accuracy: 0.8434
Validation Start Logits Accuracy: 0.8909
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': 4176, '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
1.7701	0.5788	0.6180	0.5240	0.8406	0.8811	0
0.9970	0.7439	0.7841	0.4812	0.8434	0.8979	1
0.7421	0.8018	0.8399	0.4881	0.8434	0.8909	2

Framework versions

Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.4
Tokenizers 0.13.3