t5-small-finetuned_xsum

pki/t5-small-finetuned_xsum

t5-small-finetuned_xsum is a machine learning model from pki. 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. --

Downloads · 30 days

1% of all-time downloads

All-time downloads

14.4K

Public

Parameters

60.5M

617 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin242 MB · 50%

At a glance

Library: transformers
Model type: t5
Access: Public
Created: Mar 2, 2022
Updated: Jul 19, 2023
SHA: 2239ec3b

Datasets

xsum

Model card

From the Hugging Face model README

t5-small-finetuned_xsum

This model is a fine-tuned version of pki/t5-small-finetuned_xsum on the xsum dataset. It achieves the following results on the evaluation set:

Loss: 2.0479
Rouge1: 34.0559
Rouge2: 12.7506
Rougel: 27.6762
Rougelsum: 27.68
Gen Len: 18.7924

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.1176	1.0	12753	2.0913	33.1548	11.8434	26.7805	26.7751	18.7805
2.1019	2.0	25506	2.0875	33.231	11.9329	26.8674	26.861	18.7992
2.1044	3.0	38259	2.0846	33.3643	11.9807	26.9817	26.9764	18.773
2.0874	4.0	51012	2.0832	33.3562	12.0681	27.0178	27.0189	18.7988
2.0791	5.0	63765	2.0803	33.38	12.081	27.0368	27.0344	18.7844
2.0894	6.0	76518	2.0787	33.2549	11.9662	26.8674	26.8669	18.7975
2.0802	7.0	89271	2.0777	33.3978	12.0828	27.0461	27.0443	18.7757
2.0719	8.0	102024	2.0743	33.4083	12.1141	27.0523	27.0457	18.7928
2.0782	9.0	114777	2.0748	33.3673	12.1637	27.0696	27.0663	18.7902
2.0736	10.0	127530	2.0713	33.5771	12.2219	27.1707	27.1706	18.7945
2.0816	11.0	140283	2.0703	33.5099	12.2069	27.1822	27.1835	18.8002
2.057	12.0	153036	2.0693	33.5853	12.2427	27.2096	27.2109	18.806
2.0584	13.0	165789	2.0676	33.4883	12.2674	27.1582	27.154	18.7857
2.0475	14.0	178542	2.0662	33.5529	12.2765	27.1897	27.1901	18.79
2.0426	15.0	191295	2.0643	33.6543	12.3545	27.2946	27.2928	18.8036
2.0373	16.0	204048	2.0648	33.6671	12.349	27.2649	27.2707	18.7905
2.0178	17.0	216801	2.0637	33.6794	12.4545	27.3015	27.3079	18.7948
2.0235	18.0	229554	2.0626	33.7635	12.423	27.3475	27.3446	18.7892
2.0296	19.0	242307	2.0622	33.7574	12.4651	27.3879	27.3882	18.8134
2.0319	20.0	255060	2.0595	33.9093	12.5389	27.5003	27.5001	18.7915
2.0208	21.0	267813	2.0583	33.7875	12.4912	27.4243	27.4332	18.7982
2.0151	22.0	280566	2.0581	33.8516	12.4805	27.46	27.4647	18.816
2.0188	23.0	293319	2.0575	33.7744	12.4548	27.381	27.382	18.802
2.0087	24.0	306072	2.0579	33.8953	12.4984	27.4675	27.4727	18.7819
2.0186	25.0	318825	2.0557	33.7766	12.4414	27.4025	27.4024	18.8005
2.0051	26.0	331578	2.0555	33.8973	12.5796	27.5338	27.5339	18.8153
2.0024	27.0	344331	2.0557	33.8709	12.5116	27.4684	27.4664	18.7911
1.9947	28.0	357084	2.0545	33.8499	12.5242	27.4677	27.4716	18.8025
1.9931	29.0	369837	2.0545	33.7957	12.5272	27.4129	27.4174	18.8
1.9826	30.0	382590	2.0548	33.9723	12.6665	27.5598	27.5662	18.7958
1.999	31.0	395343	2.0522	33.9702	12.6435	27.5788	27.579	18.795
1.9872	32.0	408096	2.0525	33.9546	12.638	27.5985	27.5949	18.7976
1.991	33.0	420849	2.0520	33.9792	12.6073	27.5686	27.5707	18.8056
2.0044	34.0	433602	2.0504	34.0736	12.6511	27.647	27.6472	18.8093
1.9972	35.0	446355	2.0513	34.0506	12.711	27.6533	27.6537	18.7984
1.9901	36.0	459108	2.0504	33.9991	12.638	27.626	27.6272	18.7996
1.9742	37.0	471861	2.0507	33.9357	12.6636	27.5673	27.5716	18.8064
1.984	38.0	484614	2.0502	33.9476	12.6589	27.58	27.5813	18.8037
1.9864	39.0	497367	2.0499	34.0733	12.7198	27.6926	27.6992	18.8061
1.9734	40.0	510120	2.0492	33.9483	12.6486	27.5571	27.5598	18.8033
1.9895	41.0	522873	2.0490	33.9753	12.684	27.6058	27.6086	18.8011
1.964	42.0	535626	2.0487	33.9528	12.6376	27.576	27.5824	18.7919
1.9849	43.0	548379	2.0487	33.9868	12.6936	27.6116	27.6158	18.7966
1.9798	44.0	561132	2.0491	34.0379	12.7161	27.6227	27.6315	18.7889
1.9837	45.0	573885	2.0473	34.0046	12.6559	27.5931	27.5988	18.7996
1.9556	46.0	586638	2.0483	34.0378	12.712	27.6346	27.6446	18.7942
1.9844	47.0	599391	2.0479	34.0301	12.7121	27.6492	27.6554	18.7999
1.9869	48.0	612144	2.0474	34.0463	12.7151	27.6542	27.6604	18.7919
1.9851	49.0	624897	2.0476	34.0549	12.7384	27.6542	27.6555	18.7924
1.9912	50.0	637650	2.0479	34.0559	12.7506	27.6762	27.68	18.7924

Framework versions

Transformers 4.12.0.dev0
Pytorch 1.10.1
Datasets 1.14.0
Tokenizers 0.10.3

Library: transformers
Type: t5
Created: Mar 2, 2022
Updated: Jul 19, 2023