deberta-v3-base-tuned – AI Model by gnurt2041 | AlphaNeural AI

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gnurt2041/
deberta-v3-base-tuned

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text-classification

generated_from_trainer

microsoft/deberta-v3-base

autotrain_compatible

endpoints_compatible

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deberta-v3-base

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: 0.1028
Accuracy: 0.9667
Precision: 0.9672
Recall: 0.9667
F1: 0.9667

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: 3e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.3225	0.9895	59	0.3013	0.9083	0.9135	0.9083	0.9077
0.126	1.9958	119	0.2186	0.9167	0.9188	0.9167	0.9167
0.0296	2.9853	178	0.1028	0.9667	0.9672	0.9667	0.9667
0.0564	3.9916	238	0.1482	0.975	0.9751	0.975	0.9750
0.0755	4.9979	298	0.1509	0.95	0.9506	0.95	0.9500
0.0015	5.9874	357	0.1600	0.9667	0.9667	0.9667	0.9667
0.0086	6.9937	417	0.2844	0.9417	0.9475	0.9417	0.9413
0.0011	8.0	477	0.1715	0.9667	0.9667	0.9667	0.9667
0.0009	8.9895	536	0.1922	0.9667	0.9667	0.9667	0.9667
0.001	9.8952	590	0.1981	0.9667	0.9667	0.9667	0.9667

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.0.2
Tokenizers 0.19.1