RoBERTa-FineTune-50-50 – AI Model by Shaurya-Shsin | AlphaNeural AI

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Shaurya-Shsin/
RoBERTa-FineTune-50-50

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RoBERTa-FineTune-50-50

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

Loss: 0.2524
Accuracy: 0.9075
Precision: 0.2084
Recall: 0.7415
F1: 0.3254
F2: 0.4906

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: 9.4e-06
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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	F2
0.5122	1.0	2513	0.5259	0.7863	0.1099	0.8594	0.1949	0.3635
0.4112	2.0	5026	0.2524	0.9075	0.2084	0.7415	0.3254	0.4906
0.3594	3.0	7539	0.5030	0.8251	0.1311	0.8555	0.2274	0.4064
0.3368	4.0	10052	0.6224	0.8455	0.1442	0.8378	0.2460	0.4270
0.3113	5.0	12565	0.6899	0.8471	0.1448	0.8322	0.2467	0.4270

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1