TokenizerTestingMTSUFall2024SoftwareEngineering – AI Model by cheaptrix | AlphaNeural AI

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TokenizerTestingMTSUFall2024SoftwareEngineering

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text2text-generation

generated_from_trainer

google-t5/t5-small

text-generation-inference

endpoints_compatible

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TokenizerTestingMTSUFall2024SoftwareEngineering

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

Loss: 1.5198
Rouge1: 0.2778
Rouge2: 0.2234
Rougel: 0.2686
Rougelsum: 0.2686
Gen Len: 18.9697

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.8333	1.0	12429	1.6354	0.2717	0.2139	0.262	0.262	18.9751
1.7368	2.0	24858	1.5610	0.2763	0.2208	0.267	0.267	18.9735
1.6978	3.0	37287	1.5291	0.2777	0.2227	0.2683	0.2682	18.9699
1.7008	4.0	49716	1.5198	0.2778	0.2234	0.2686	0.2686	18.9697

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1