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reberta-base-klue-ynat-classfication – AI Model by HyeonSuJJang5 | AlphaNeural AI
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HyeonSuJJang5
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reberta-base-klue-ynat-classfication
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transformers
tensorboard
safetensors
roberta
text-classification
generated_from_trainer
klue/roberta-base
finetune
autotrain_compatible
endpoints_compatible
us
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reberta-base-klue-ynat-classfication
This model is a fine-tuned version of
klue/roberta-base
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.5943
Confidence: [[-1.1646565 -1.2498165 0.29182994 ... -1.0066916 -1.6406983 -1.7746325 ] [-1.9457322 -1.8257982 0.79716635 ... 0.10126656 -1.1954982 5.9838986 ] [-0.48572934 5.6094513 0.43222797 ... -0.75162756 -2.0966194 -1.5772848 ] ... [-1.1721573 -1.0671966 -0.65719104 ... 0.11196741 6.3824944 -0.8935116 ] [-1.0086813 -0.68413603 4.671994 ... -1.6908021 -1.5391526 -0.3495633 ] [-1.1721573 -1.0671966 -0.65719104 ... 0.11196741 6.3824944 -0.8935116 ]]
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:
learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2
Training results
Framework versions
Transformers 4.53.2
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.2
