bert2bert_law_summarization_tr

This model is a fine-tuned version of mrm8488/bert2bert_shared-turkish-summarization on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9682
• Rouge1: 0.6221
• Rouge2: 0.5799
• Rougel: 0.6007
• Rougelsum: 0.6009
• Gen Len: 63.3077

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: 2
• eval_batch_size: 2
• 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
1.6025	1.0	520	1.1210	0.611	0.5687	0.5885	0.5883	62.6154
1.0845	2.0	1040	1.0161	0.62	0.5789	0.601	0.6005	63.4038
0.9252	3.0	1560	0.9784	0.6249	0.583	0.6043	0.6045	63.2654
0.858	4.0	2080	0.9682	0.6221	0.5799	0.6007	0.6009	63.3077

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.0
• Tokenizers 0.13.3