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Kuongan/
CS221-xlm-roberta-base-som-noaug-finetuned-som-tapt

Name: CS221-xlm-roberta-base-som-noaug-finetuned-som-tapt
Availability: InStock

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Kuongan/xlm-roberta-base-som-noaug

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CS221-xlm-roberta-base-som-noaug-finetuned-som-tapt

This model is a fine-tuned version of Kuongan/xlm-roberta-base-som-noaug on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1389
F1: 0.7193
Roc Auc: 0.8401
Accuracy: 0.7427

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
0.163	1.0	142	0.1401	0.6650	0.8215	0.7436
0.1637	2.0	284	0.1389	0.7193	0.8401	0.7427
0.1492	3.0	426	0.1447	0.6721	0.8107	0.7312
0.1194	4.0	568	0.1518	0.6364	0.7957	0.7206
0.1106	5.0	710	0.1516	0.6468	0.7956	0.7250

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Kuongan/CS221-xlm-roberta-base-som-noaug-finetuned-som-tapt

CS221-xlm-roberta-base-som-noaug-finetuned-som-tapt

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Kuongan/
CS221-xlm-roberta-base-som-noaug-finetuned-som-tapt