ptt5-wikilingua-gptextsum

arthurmluz/ptt5-wikilingua-gptextsum

ptt5-wikilingua-gptextsum is a machine learning model from arthurmluz. 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. The card lists the license as mit.

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

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Hugging Face

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At a glance

Library: transformers
License: mit
Model type: t5
Access: Public
Created: Nov 8, 2023
Updated: Nov 27, 2024
SHA: e903aa0c

Base models

arthurmluz/ptt5-wikilingua-30epochs

Model card

From the Hugging Face model README

ptt5-wikilingua-gptextsum

This model is a fine-tuned version of arthurmluz/ptt5-wikilingua-30epochs on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.1738
Rouge1: 0.1741
Rouge2: 0.0918
Rougel: 0.1451
Rougelsum: 0.1624
Gen Len: 19.0

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	70	2.3792	0.1569	0.0643	0.1218	0.1443	19.0
No log	2.0	140	2.2867	0.1786	0.0801	0.1329	0.162	19.0
2.4042	3.0	210	2.2380	0.1653	0.0682	0.1244	0.152	19.0
2.4042	4.0	280	2.2055	0.1708	0.0744	0.1302	0.1575	19.0
2.4042	5.0	350	2.1882	0.173	0.0804	0.139	0.1609	19.0
2.0557	6.0	420	2.1724	0.1779	0.0846	0.1385	0.1636	19.0
2.0557	7.0	490	2.1614	0.175	0.0841	0.1359	0.1634	19.0
2.0557	8.0	560	2.1537	0.1729	0.0823	0.1348	0.1596	19.0
1.866	9.0	630	2.1512	0.1743	0.0863	0.1387	0.1637	19.0
1.866	10.0	700	2.1464	0.1735	0.0866	0.1382	0.1608	19.0
1.866	11.0	770	2.1398	0.1748	0.0876	0.1383	0.1616	19.0
1.7225	12.0	840	2.1424	0.1752	0.0881	0.1426	0.1611	19.0
1.7225	13.0	910	2.1431	0.173	0.0865	0.1414	0.1592	19.0
1.7225	14.0	980	2.1481	0.1713	0.0865	0.1413	0.1585	19.0
1.606	15.0	1050	2.1430	0.1694	0.0834	0.1384	0.1557	19.0
1.606	16.0	1120	2.1501	0.1676	0.0841	0.1365	0.1538	19.0
1.606	17.0	1190	2.1526	0.1698	0.0866	0.1398	0.1561	19.0
1.5257	18.0	1260	2.1536	0.1749	0.0925	0.1441	0.1607	19.0
1.5257	19.0	1330	2.1564	0.1756	0.0945	0.1451	0.162	19.0
1.4673	20.0	1400	2.1594	0.1758	0.0949	0.1451	0.1616	19.0
1.4673	21.0	1470	2.1623	0.1762	0.0943	0.1466	0.1624	19.0
1.4673	22.0	1540	2.1644	0.1762	0.0943	0.147	0.1627	19.0
1.4108	23.0	1610	2.1672	0.1768	0.0955	0.1473	0.1635	19.0
1.4108	24.0	1680	2.1689	0.1751	0.0933	0.1459	0.1633	19.0
1.4108	25.0	1750	2.1694	0.1751	0.0933	0.1459	0.1633	19.0
1.384	26.0	1820	2.1701	0.1751	0.0933	0.1459	0.1633	19.0
1.384	27.0	1890	2.1723	0.1747	0.0937	0.1449	0.1626	19.0
1.384	28.0	1960	2.1737	0.1743	0.0925	0.1445	0.1623	19.0
1.3507	29.0	2030	2.1738	0.1741	0.0918	0.1451	0.1624	19.0
1.3507	30.0	2100	2.1738	0.1741	0.0918	0.1451	0.1624	19.0

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1

Library: transformers
Type: t5
License: mit
Languages: pt
Created: Nov 8, 2023
Updated: Nov 27, 2024