text_shortening_model_v15

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

• Loss: 1.6707
• Rouge1: 0.5171
• Rouge2: 0.3003
• Rougel: 0.4648
• Rougelsum: 0.4666
• Bert precision: 0.8787
• Bert recall: 0.8819
• Average word count: 11.25
• Max word count: 18
• Min word count: 5
• Average token count: 16.35
• % shortened texts with length > 12: 36.4286

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bert precision	Bert recall	Average word count	Max word count	Min word count	Average token count	% shortened texts with length > 12
1.1859	1.0	62	1.6707	0.5171	0.3003	0.4648	0.4666	0.8787	0.8819	11.25	18	5	16.35	36.4286

Framework versions

• Transformers 4.33.1
• Pytorch 2.0.1+cpu
• Datasets 2.14.5
• Tokenizers 0.13.3