bert2bert_law_summarization

tulayturanmaku/bert2bert_law_summarization

bert2bert_law_summarization 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: Aug 16, 2023
Updated: Aug 16, 2023
SHA: 3309291f

Model card

From the Hugging Face model README

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

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