indobert-summarization-bert2bert

Alfahluzi/indobert-summarization-bert2bert

indobert-summarization-bert2bert is a machine learning model from Alfahluzi. 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: Apr 20, 2024
Updated: Apr 20, 2024
SHA: 91ac667c

Datasets

id_liputan6

Model card

From the Hugging Face model README

bert2bert-model99-last

This model is a fine-tuned version of on the id_liputan6 dataset. It achieves the following results on the evaluation set:

Loss: 2.8701
R1 Precision: 0.3001
R1 Recall: 0.34
R1 Fmeasure: 0.3156
R2 Precision: 0.121
R2 Recall: 0.1366
R2 Fmeasure: 0.1269
Rl Precision: 0.239
Rl Recall: 0.2707
Rl Fmeasure: 0.2513

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: 5e-05
train_batch_size: 18
eval_batch_size: 18
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	R1 Precision	R1 Recall	R1 Fmeasure	R2 Precision	R2 Recall	R2 Fmeasure	Rl Precision	Rl Recall	Rl Fmeasure
2.3429	1.0	10772	2.7616	0.29	0.3334	0.3069	0.1175	0.1351	0.1243	0.2329	0.2678	0.2464
1.5227	2.0	21544	2.6637	0.287	0.3356	0.3062	0.1148	0.1338	0.1222	0.2304	0.2693	0.2457
1.3203	3.0	32316	2.6384	0.2934	0.3387	0.3111	0.1195	0.1377	0.1265	0.2355	0.272	0.2498
1.169	4.0	43088	2.6579	0.3004	0.3403	0.3158	0.1228	0.139	0.129	0.2407	0.2726	0.253
1.0416	5.0	53860	2.6894	0.2963	0.3367	0.3121	0.1202	0.1362	0.1264	0.2367	0.2691	0.2494
0.9303	6.0	64632	2.7418	0.2986	0.3417	0.3155	0.1213	0.1384	0.1279	0.2385	0.2727	0.2519
0.8375	7.0	75404	2.8060	0.3009	0.3417	0.3168	0.1223	0.1384	0.1285	0.2402	0.2727	0.2528
0.7675	8.0	86176	2.8701	0.3001	0.34	0.3156	0.121	0.1366	0.1269	0.239	0.2707	0.2513

Framework versions

Transformers 4.40.0
Pytorch 2.2.1
Datasets 2.19.0
Tokenizers 0.19.1

Library: transformers
Type: encoder-decoder
Created: Apr 20, 2024
Updated: Apr 20, 2024