bert2bert_law_summarization_tr

tulayturanmaku/bert2bert_law_summarization_tr

bert2bert_law_summarization_tr is a machine learning model from tulayturanmaku. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Model type: encoder-decoder
Access: Public
Created: Jun 16, 2023
Updated: Jun 16, 2023
SHA: 0db6beaa

Model card

From the Hugging Face model README

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

Library: transformers
Type: encoder-decoder
Created: Jun 16, 2023
Updated: Jun 16, 2023