t5-small-finetuned-summarization-billsum – AI Model by hasan-mr | AlphaNeural AI

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hasan-mr/
t5-small-finetuned-summarization-billsum

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text2text-generation

generated_from_keras_callback

google-t5/t5-small

endpoints_compatible

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hasan-mr/t5-small-finetuned-summarization-billsum

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

Train Loss: 2.5791
Validation Loss: 2.3660
Train Rougel: tf.Tensor(0.129898, shape=(), dtype=float32)
Epoch: 3

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:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: mixed_float16

Training results

Train Loss	Validation Loss	Train Rougel	Epoch
3.3746	2.7009	tf.Tensor(0.10603927, shape=(), dtype=float32)	0
2.8289	2.5104	tf.Tensor(0.108828835, shape=(), dtype=float32)	1
2.6672	2.4200	tf.Tensor(0.1210279, shape=(), dtype=float32)	2
2.5791	2.3660	tf.Tensor(0.129898, shape=(), dtype=float32)	3

Framework versions

Transformers 4.34.0
TensorFlow 2.14.0
Datasets 2.14.5
Tokenizers 0.14.1