google-t5-t5-base_finetuned_augmented_augmented_nemotron-mini_4b – AI Model by jackmedda | AlphaNeural AI

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jackmedda/
google-t5-t5-base_finetuned_augmented_augmented_nemotron-mini_4b

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text-classification

generated_from_trainer

google-t5/t5-base

autotrain_compatible

text-generation-inference

endpoints_compatible

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jackmedda/google-t5-t5-base_finetuned_augmented_augmented_nemotron-mini_4b

This model is a fine-tuned version of google-t5/t5-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5670
Accuracy: 0.7647
F1: 0.8667
Precision: 0.7647
Recall: 1.0

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: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.5979	1.0	11	0.6165	0.7	0.8235	0.7	1.0
0.4517	2.0	22	0.6603	0.7	0.8235	0.7	1.0
0.4161	3.0	33	0.7237	0.7	0.8235	0.7	1.0
0.4372	4.0	44	0.7463	0.7	0.8235	0.7	1.0
0.3933	5.0	55	0.7271	0.7	0.8235	0.7	1.0
0.4394	6.0	66	0.7156	0.7	0.8235	0.7	1.0
0.405	7.0	77	0.7025	0.7	0.8235	0.7	1.0
0.382	8.0	88	0.7081	0.7	0.8235	0.7	1.0
0.3631	9.0	99	0.7368	0.7	0.8235	0.7	1.0

Framework versions

Transformers 4.48.2
Pytorch 2.3.0+cu121
Datasets 3.2.0
Tokenizers 0.21.0