conplag1_graphcodebert_ep30_bs16_lr1e-05_l512_s42_ppy_loss – AI Model by buelfhood | AlphaNeural AI

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buelfhood/
conplag1_graphcodebert_ep30_bs16_lr1e-05_l512_s42_ppy_loss

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text-classification

generated_from_trainer

microsoft/graphcodebert-base

text-embeddings-inference

endpoints_compatible

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conplag1_graphcodebert_ep30_bs16_lr1e-05_l512_s42_ppy_loss

This model is a fine-tuned version of microsoft/graphcodebert-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4643
Accuracy: 0.8394
Recall: 0.6316
Precision: 0.75
F1: 0.6857
F Beta Score: 0.6638

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1	F Beta Score
0.6724	1.0	40	0.6220	0.6788	0.7632	0.4531	0.5686	0.6304
0.5739	2.0	80	0.5048	0.7956	0.6842	0.6190	0.65	0.6627
0.4548	3.0	120	0.4643	0.8394	0.6316	0.75	0.6857	0.6638
0.343	4.0	160	0.6242	0.8321	0.4737	0.8571	0.6102	0.5493
0.4401	5.0	200	0.4801	0.8321	0.6579	0.7143	0.6849	0.6743
0.3385	6.0	240	0.4889	0.8467	0.6316	0.7742	0.6957	0.6695

Framework versions

Transformers 4.51.3
Pytorch 2.8.0+cu128
Datasets 3.1.0
Tokenizers 0.21.4