t5-small-billsum – AI Model by antonkurylo | AlphaNeural AI

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antonkurylo/
t5-small-billsum

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text2text-generation

generated_from_trainer

google-t5/t5-small

text-generation-inference

endpoints_compatible

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t5-small-billsum

This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.9564
Rouge1: 50.3551
Rouge2: 29.3717
Rougel: 39.4102
Rougelsum: 43.6247

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.5468	1.0	1185	2.0937	48.625	27.492	37.671	41.4628
2.2867	2.0	2370	2.0155	49.2547	28.248	38.39	42.3374
2.2241	3.0	3555	1.9796	49.8802	28.8333	38.8829	43.027
2.1925	4.0	4740	1.9620	50.07	28.9961	39.1086	43.3251
2.1791	5.0	5925	1.9576	50.2626	29.1819	39.2415	43.4781

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.2
Tokenizers 0.19.1