summarise

chinh210602/summarise

summarise is a machine learning model from chinh210602. 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: mbart
Access: Public
Created: May 24, 2023
Updated: May 24, 2023
SHA: b0368032

Model card

From the Hugging Face model README

summarise

This model is a fine-tuned version of vinai/bartpho-word-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.2042
Rouge1: 0.3976
Rouge2: 0.1671
Rougel: 0.2789
Rougelsum: 0.2783
Gen Len: 19.7789

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: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	0.99	97	1.5996	0.2912	0.1103	0.2037	0.203	15.7121
No log	2.0	195	1.3741	0.3885	0.1547	0.2714	0.2714	19.7044
No log	2.99	292	1.3240	0.3687	0.1505	0.2624	0.2624	18.9743
No log	4.0	390	1.2874	0.3929	0.1611	0.2775	0.2771	19.91
No log	4.99	487	1.3065	0.3829	0.1552	0.2667	0.2664	19.3059
2.392	6.0	585	1.2555	0.3836	0.1549	0.2689	0.2686	19.6452
2.392	6.99	682	1.2605	0.3796	0.157	0.2665	0.2663	19.3496
2.392	8.0	780	1.2517	0.3969	0.1621	0.2787	0.2781	19.8946
2.392	8.99	877	1.2349	0.3885	0.1593	0.2713	0.2709	19.7326
2.392	10.0	975	1.2197	0.3864	0.1554	0.2701	0.2695	19.4936
1.2799	10.99	1072	1.2143	0.3805	0.1565	0.2682	0.2675	19.5141
1.2799	12.0	1170	1.2111	0.391	0.1594	0.2729	0.2725	19.6992
1.2799	12.99	1267	1.1993	0.3849	0.1568	0.2686	0.2681	19.6632
1.2799	14.0	1365	1.2196	0.3888	0.1587	0.2733	0.273	19.6915
1.2799	14.99	1462	1.1952	0.3947	0.1611	0.2754	0.2747	19.8021
1.2178	16.0	1560	1.1982	0.3954	0.1652	0.2778	0.2775	19.8689
1.2178	16.99	1657	1.1988	0.3957	0.1641	0.2765	0.276	19.7121
1.2178	18.0	1755	1.2020	0.3998	0.169	0.281	0.2806	19.7815
1.2178	18.99	1852	1.1995	0.3965	0.1662	0.2782	0.2778	19.8226
1.2178	19.9	1940	1.2042	0.3976	0.1671	0.2789	0.2783	19.7789

Framework versions

Transformers 4.30.0.dev0
Pytorch 2.0.0
Datasets 2.12.0
Tokenizers 0.13.3

Library: transformers
Type: mbart
Created: May 24, 2023
Updated: May 24, 2023