NLI_UniversalClassifier_beta – AI Model by N1CKNGUYEN | AlphaNeural AI

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N1CKNGUYEN/
NLI_UniversalClassifier_beta

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text-classification

generated_from_trainer

microsoft/deberta-v3-base

autotrain_compatible

endpoints_compatible

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nli_classifier

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3715
F1 Macro: 0.8526
F1 Micro: 0.8656
Accuracy Balanced: 0.8527
Accuracy: 0.8656
Precision Macro: 0.8526
Recall Macro: 0.8527
Precision Micro: 0.8656
Recall Micro: 0.8656

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: 16
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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
lr_scheduler_warmup_ratio: 0.06
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	Accuracy Balanced	Accuracy	Precision Macro	Recall Macro	Precision Micro	Recall Micro
0.2235	1.0	9145	0.3456	0.8493	0.8627	0.8488	0.8627	0.8497	0.8488	0.8627	0.8627
0.1575	1.9998	18288	0.3715	0.8526	0.8656	0.8527	0.8656	0.8526	0.8527	0.8656	0.8656

Framework versions

Transformers 4.51.3
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.0