codegen-350M-mono-measurement_pred-diamonds-seed3 – AI Model by oliverdk | AlphaNeural AI

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oliverdk/
codegen-350M-mono-measurement_pred-diamonds-seed3

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Salesforce/codegen-350M-mono

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codegen-350M-mono-measurement_pred-diamonds-seed3

This model is a fine-tuned version of Salesforce/codegen-350M-mono on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3757
Accuracy: 0.9134
Accuracy Sensor 0: 0.9235
Auroc Sensor 0: 0.9559
Accuracy Sensor 1: 0.8989
Auroc Sensor 1: 0.9539
Accuracy Sensor 2: 0.9486
Auroc Sensor 2: 0.9653
Accuracy Aggregated: 0.8826
Auroc Aggregated: 0.9553

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 64
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Accuracy Sensor 0	Auroc Sensor 0	Accuracy Sensor 1	Auroc Sensor 1	Accuracy Sensor 2	Auroc Sensor 2	Accuracy Aggregated	Auroc Aggregated
0.287	0.9997	781	0.4392	0.8094	0.8151	0.8977	0.8235	0.9036	0.8395	0.9106	0.7594	0.8793
0.2108	1.9994	1562	0.2409	0.9058	0.9011	0.9242	0.9062	0.9344	0.9238	0.9424	0.8920	0.9178
0.1549	2.9990	2343	0.2347	0.9119	0.9185	0.9519	0.8929	0.9546	0.9481	0.9605	0.8883	0.9476
0.0887	4.0	3125	0.2867	0.9139	0.9243	0.9558	0.9057	0.9547	0.9473	0.9653	0.8785	0.9543
0.0444	4.9984	3905	0.3757	0.9134	0.9235	0.9559	0.8989	0.9539	0.9486	0.9653	0.8826	0.9553

Framework versions

Transformers 4.41.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1