outputs – AI Model by pglee | AlphaNeural AI

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text-classification

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

Loss: 0.0926
Accuracy: 0.8780
F1: 0.3881
Precision: 0.5417
Recall: 0.3023

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: 8e-05
train_batch_size: 256
eval_batch_size: 512
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	6	0.0874	0.8810	0.4118	0.56	0.3256
No log	2.0	12	0.0936	0.8839	0.4000	0.5909	0.3023
No log	3.0	18	0.0922	0.8780	0.3881	0.5417	0.3023
No log	4.0	24	0.0926	0.8780	0.3881	0.5417	0.3023

Framework versions

Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1