text-summarization – AI Model by scottn66 | AlphaNeural AI

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scottn66/
text-summarization

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text2text-generation

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text-generation-inference

endpoints_compatible

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text-summarization

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

Loss: 2.4284
Rouge1: 0.1405
Rouge2: 0.0517
Rougel: 0.1158
Rougelsum: 0.1157
Gen Len: 19.0

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: 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
No log	1.0	62	2.7231	0.1246	0.0356	0.1039	0.1039	19.0
No log	2.0	124	2.5099	0.1335	0.0463	0.1116	0.1116	19.0
No log	3.0	186	2.4451	0.1383	0.0509	0.114	0.114	19.0
No log	4.0	248	2.4284	0.1405	0.0517	0.1158	0.1157	19.0

Framework versions

Transformers 4.27.4
Pytorch 1.13.1+cu116
Datasets 2.11.0
Tokenizers 0.13.2