bert2bert_law_summarization

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: 1.1184
• Rouge1: 0.6064
• Rouge2: 0.5608
• Rougel: 0.5828
• Rougelsum: 0.5836
• Gen Len: 63.2615

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
1.5546	1.0	520	1.2699	0.6047	0.5588	0.5795	0.5799	62.7038
1.071	2.0	1040	1.1607	0.6075	0.5598	0.5814	0.5824	63.2269
0.9101	3.0	1560	1.1268	0.6129	0.569	0.5884	0.5891	62.9654
0.798	4.0	2080	1.1184	0.6064	0.5608	0.5828	0.5836	63.2615

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3