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CS221-xlm-roberta-base-sun-noaug-finetuned-sun-tapt – AI Model by Kuongan | AlphaNeural AI
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Kuongan
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CS221-xlm-roberta-base-sun-noaug-finetuned-sun-tapt
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transformers
safetensors
xlm-roberta
text-classification
generated_from_trainer
Kuongan/xlm-roberta-base-sun-noaug
finetune
mit
autotrain_compatible
endpoints_compatible
us
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CS221-xlm-roberta-base-sun-noaug-finetuned-sun-tapt
This model is a fine-tuned version of
Kuongan/xlm-roberta-base-sun-noaug
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2988
F1: 0.1537
Roc Auc: 0.5
Accuracy: 0.7195
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: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20
Training results
Training Loss
Epoch
Step
Validation Loss
F1
Roc Auc
Accuracy
0.4561
1.0
52
0.2988
0.1537
0.5
0.7195
0.3015
2.0
104
0.2725
0.1537
0.5
0.7195
0.2883
3.0
156
0.2727
0.1537
0.5
0.7195
0.2817
4.0
208
0.2724
0.1537
0.5
0.7195
Framework versions
Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0
