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results – AI Model by Swaifa | AlphaNeural AI
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Swaifa
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results
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transformers
safetensors
roberta
text-classification
generated_from_trainer
microsoft/codebert-base
finetune
autotrain_compatible
endpoints_compatible
us
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results
This model is a fine-tuned version of
microsoft/codebert-base
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.4713
Accuracy: 0.785
F1: 0.8028
Precision: 0.7709
Recall: 0.8373
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: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 4
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
No log
1.0
90
0.6563
0.6188
0.5612
0.8125
0.4286
No log
2.0
180
0.4323
0.8
0.8222
0.8315
0.8132
No log
3.0
270
0.3549
0.8625
0.875
0.9059
0.8462
Framework versions
Transformers 4.54.1
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.21.4
