MTSUSpring2025SoftwareEngineering – AI Model by cheaptrix | AlphaNeural AI | AlphaNeural AI

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cheaptrix/
MTSUSpring2025SoftwareEngineering

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text2text-generation

generated_from_trainer

google-t5/t5-small

text-generation-inference

endpoints_compatible

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MTSUSpring2025SoftwareEngineering

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: 1.4151
Rouge1: 0.3167
Rouge2: 0.2556
Rougel: 0.3052
Rougelsum: 0.3051
Gen Len: 19.8434

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: Use OptimizerNames.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: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.7529	1.0	14778	1.5535	0.3091	0.2415	0.2959	0.2958	19.8355
1.6541	2.0	29556	1.4777	0.313	0.2491	0.3006	0.3006	19.8419
1.602	3.0	44334	1.4397	0.3155	0.2534	0.3036	0.3036	19.8513
1.6015	4.0	59112	1.4211	0.3164	0.2552	0.3049	0.3049	19.84
1.569	5.0	73890	1.4151	0.3167	0.2556	0.3052	0.3051	19.8434

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0