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

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vnktrmnb/
xlm-roberta-base-FT-TyDiQA-GoldP_PP

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vnktrmnb/xlm-roberta-base-FT-TyDiQA-GoldP_PP

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: 0.5137
Train End Logits Accuracy: 0.8283
Train Start Logits Accuracy: 0.8820
Validation Loss: 0.4928
Validation End Logits Accuracy: 0.8587
Validation Start Logits Accuracy: 0.9105
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': 4632, '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
1.6906	0.5681	0.6167	0.5863	0.8126	0.8657	0
0.8028	0.7600	0.8122	0.4847	0.8503	0.9077	1
0.5137	0.8283	0.8820	0.4928	0.8587	0.9105	2

Framework versions

Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.4
Tokenizers 0.13.3