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vnktrmnb/
xlm-roberta-base-FT-TyDiQA_AUQC-FT-TyDiQA_AUQC

Name: xlm-roberta-base-FT-TyDiQA_AUQC-FT-TyDiQA_AUQC
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vnktrmnb/xlm-roberta-base-FT-TyDiQA_AUQC-FT-TyDiQA_AUQC

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

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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': 9744, '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

Train Loss	Train End Logits Accuracy	Train Start Logits Accuracy	Validation Loss	Validation End Logits Accuracy	Validation Start Logits Accuracy	Epoch
0.7982	0.7889	0.8252	0.4654	0.8587	0.9119	0
0.6087	0.8296	0.8619	0.4771	0.8587	0.9063	1
0.4630	0.8631	0.8954	0.5086	0.8671	0.9119	2
0.3575	0.8913	0.9164	0.5528	0.8615	0.8993	3
0.2690	0.9131	0.9313	0.5861	0.8545	0.9035	4
0.2047	0.9312	0.9485	0.6629	0.8601	0.9021	5
0.1588	0.9455	0.9579	0.6655	0.8615	0.9035	6