CodeDetect-BERT-T2 – AI Model by azherali | AlphaNeural AI

You can deploy this model and start earning money today!

azherali/
CodeDetect-BERT-T2

like

0

text-classification

generated_from_trainer

google-bert/bert-base-uncased

text-embeddings-inference

endpoints_compatible

Views

No views yet

CodeDetect-BERT-T2

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

Loss: 2.2616
Accuracy: 0.9178
F1: 0.4680
Precision: 0.4960
Recall: 0.4600

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: 128
eval_batch_size: 128
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.1731	1.2798	5000	2.2130	0.9197	0.3964	0.4880	0.3890
1.8504	2.5595	10000	2.1255	0.9216	0.4423	0.5119	0.4239
1.5734	3.8393	15000	2.1121	0.9226	0.4766	0.5175	0.4584
1.4124	5.0	19535	2.2616	0.9178	0.4680	0.4960	0.4600

Framework versions

Transformers 5.13.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2