deberta-v3-base_finetuned_nostalgia – AI Model by beja1996 | AlphaNeural AI

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beja1996/
deberta-v3-base_finetuned_nostalgia

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text-classification

generated_from_trainer

microsoft/deberta-v3-base

autotrain_compatible

endpoints_compatible

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deberta-v3-base_finetuned_nostalgia

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3772
Accuracy: 0.9379
F1 Macro: 0.9288
Accuracy Balanced: 0.9264
F1 Micro: 0.9379
Precision Macro: 0.9313
Recall Macro: 0.9264
Precision Micro: 0.9379
Recall Micro: 0.9379

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: 64
seed: 1984
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.06
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Accuracy Balanced	F1 Micro	Precision Macro	Recall Macro	Precision Micro	Recall Micro
No log	1.0	73	0.3903	0.8759	0.8470	0.8251	0.8759	0.8890	0.8251	0.8759	0.8759
No log	2.0	146	0.2130	0.9103	0.8933	0.8783	0.9103	0.9149	0.8783	0.9103	0.9103
No log	3.0	219	0.1253	0.9379	0.9288	0.9264	0.9379	0.9313	0.9264	0.9379	0.9379
No log	4.0	292	0.2694	0.9310	0.9229	0.9324	0.9310	0.9154	0.9324	0.9310	0.9310
No log	5.0	365	0.1924	0.9448	0.9370	0.9370	0.9448	0.9370	0.9370	0.9448	0.9448
No log	6.0	438	0.2648	0.9379	0.9288	0.9264	0.9379	0.9313	0.9264	0.9379	0.9379
0.1908	7.0	511	0.3431	0.9379	0.9288	0.9264	0.9379	0.9313	0.9264	0.9379	0.9379
0.1908	8.0	584	0.3450	0.9379	0.9288	0.9264	0.9379	0.9313	0.9264	0.9379	0.9379
0.1908	9.0	657	0.3538	0.9379	0.9279	0.9209	0.9379	0.9362	0.9209	0.9379	0.9379
0.1908	10.0	730	0.3772	0.9379	0.9288	0.9264	0.9379	0.9313	0.9264	0.9379	0.9379

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1