t5-small-finetuned-xsum

gman007/t5-small-finetuned-xsum

t5-small-finetuned-xsum is a machine learning model from gman007. 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 apache-2.0.

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

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At a glance

Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Nov 18, 2023
Updated: Nov 20, 2023
SHA: 996d5852

Model card

From the Hugging Face model README

t5-small-finetuned-xsum

This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2835
Rouge1: 94.5193
Rouge2: 91.8587
Rougel: 94.3946
Rougelsum: 94.3859
Gen Len: 10.1837

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	50	0.2975	89.5414	84.9048	89.4617	89.3679	10.3367
No log	2.0	100	0.2853	90.0079	85.5952	89.9877	89.8834	10.4796
No log	3.0	150	0.2747	91.5401	87.6654	91.5258	91.5773	10.2347
No log	4.0	200	0.2593	92.016	88.3668	91.9969	91.9528	10.1429
No log	5.0	250	0.2552	91.6915	87.9495	91.6876	91.6684	10.1837
No log	6.0	300	0.2439	91.7318	87.9725	91.7426	91.7157	10.1633
No log	7.0	350	0.2470	91.6548	87.7132	91.6161	91.5751	10.1633
No log	8.0	400	0.2451	92.0067	88.2763	92.0299	91.9441	10.1633
No log	9.0	450	0.2293	91.9889	88.2649	91.9883	91.947	10.1122
0.2556	10.0	500	0.2246	92.2544	88.8312	92.1877	92.2088	9.9898
0.2556	11.0	550	0.2284	92.1123	88.581	91.9835	91.9521	10.5204
0.2556	12.0	600	0.2275	92.6483	89.6736	92.5045	92.4826	10.4388
0.2556	13.0	650	0.2158	92.4372	89.2814	92.4237	92.4247	9.9898
0.2556	14.0	700	0.2161	92.7992	89.8777	92.7538	92.7517	10.0102
0.2556	15.0	750	0.2187	92.7557	89.9213	92.7025	92.7383	10.1327
0.2556	16.0	800	0.2175	92.7262	89.8256	92.6713	92.6792	10.0306
0.2556	17.0	850	0.2187	92.2674	89.2391	92.2064	92.209	9.9694
0.2556	18.0	900	0.2256	92.7404	89.7964	92.6408	92.6074	10.4286
0.2556	19.0	950	0.2217	92.5744	89.5658	92.4734	92.412	10.1939
0.1469	20.0	1000	0.2258	92.3445	89.2658	92.2347	92.2116	10.2959
0.1469	21.0	1050	0.2213	93.1102	90.2779	93.0621	93.0124	10.0918
0.1469	22.0	1100	0.2228	92.8881	90.073	92.862	92.8322	10.2551
0.1469	23.0	1150	0.2307	93.0099	90.2918	93.0052	92.9897	10.3265
0.1469	24.0	1200	0.2228	93.2248	90.4889	93.1472	93.1784	10.0612
0.1469	25.0	1250	0.2298	93.255	90.2444	93.2404	93.1739	10.4694
0.1469	26.0	1300	0.2234	93.0399	90.255	92.9563	92.9872	10.2347
0.1469	27.0	1350	0.2255	93.2368	90.1876	93.153	93.1507	10.3571
0.1469	28.0	1400	0.2201	92.6915	89.7722	92.6324	92.6424	10.1939
0.1469	29.0	1450	0.2280	93.401	90.3752	93.3318	93.3154	10.3367
0.1027	30.0	1500	0.2173	93.7771	91.1983	93.68	93.6573	10.1633
0.1027	31.0	1550	0.2252	93.6074	90.6168	93.505	93.4857	10.4082
0.1027	32.0	1600	0.2296	93.6674	90.7324	93.6005	93.5946	10.2857
0.1027	33.0	1650	0.2259	93.6001	91.0069	93.5305	93.5142	10.2347
0.1027	34.0	1700	0.2292	93.444	90.8001	93.336	93.3684	10.2551
0.1027	35.0	1750	0.2456	93.41	90.4375	93.3556	93.3523	10.5
0.1027	36.0	1800	0.2344	93.1237	90.4054	93.0511	93.0336	10.3163
0.1027	37.0	1850	0.2278	94.4087	91.4083	94.3475	94.3676	10.1531
0.1027	38.0	1900	0.2287	94.7009	91.8733	94.6781	94.6291	10.2347
0.1027	39.0	1950	0.2375	93.9184	91.4673	93.8027	93.8265	10.2551
0.0771	40.0	2000	0.2351	94.226	91.5902	94.1304	94.1312	10.1735
0.0771	41.0	2050	0.2323	94.613	91.7764	94.5709	94.5716	10.1327
0.0771	42.0	2100	0.2430	93.6985	90.3041	93.6429	93.6496	10.5306
0.0771	43.0	2150	0.2456	93.935	91.1481	93.9268	93.8789	10.2347
0.0771	44.0	2200	0.2375	94.6173	92.013	94.5205	94.5409	10.102
0.0771	45.0	2250	0.2422	94.5768	92.013	94.4855	94.4934	10.1429
0.0771	46.0	2300	0.2457	94.2776	91.6575	94.2061	94.1713	10.1837
0.0771	47.0	2350	0.2500	94.2848	91.6963	94.2348	94.2356	10.1939
0.0771	48.0	2400	0.2527	94.4746	91.8897	94.386	94.3921	10.1735
0.0771	49.0	2450	0.2551	94.0728	91.3446	94.0777	94.0108	10.2143
0.0612	50.0	2500	0.2584	93.9956	90.8453	93.9907	93.9405	10.2857
0.0612	51.0	2550	0.2617	93.9956	90.8453	93.9907	93.9405	10.2857
0.0612	52.0	2600	0.2519	94.5768	92.013	94.4855	94.4934	10.1429
0.0612	53.0	2650	0.2562	94.1623	91.4384	94.0758	94.0637	10.2143
0.0612	54.0	2700	0.2622	94.2093	91.1799	94.1154	94.0872	10.2449
0.0612	55.0	2750	0.2634	93.9172	91.0739	93.7438	93.7714	10.2653
0.0612	56.0	2800	0.2632	94.1001	90.993	94.0589	94.078	10.2551
0.0612	57.0	2850	0.2628	94.5008	91.5524	94.3838	94.389	10.2143
0.0612	58.0	2900	0.2627	94.6598	91.6029	94.5581	94.5736	10.2959
0.0612	59.0	2950	0.2613	94.5008	91.5524	94.3838	94.389	10.2143
0.0512	60.0	3000	0.2579	94.8724	92.1655	94.7536	94.7746	10.2551
0.0512	61.0	3050	0.2584	94.5705	91.9185	94.4551	94.4894	10.1429
0.0512	62.0	3100	0.2677	94.4155	91.2837	94.3625	94.3503	10.3367
0.0512	63.0	3150	0.2662	94.5507	91.6625	94.4745	94.4616	10.3061
0.0512	64.0	3200	0.2661	94.7637	92.0025	94.6615	94.6629	10.2857
0.0512	65.0	3250	0.2662	94.4873	91.5621	94.4055	94.3919	10.2347
0.0512	66.0	3300	0.2639	94.5583	91.9442	94.4617	94.4686	10.1531
0.0512	67.0	3350	0.2630	94.6222	91.9897	94.5198	94.5502	10.1429
0.0512	68.0	3400	0.2691	94.2455	91.2383	94.216	94.1613	10.2551
0.0512	69.0	3450	0.2725	94.1025	91.0061	94.0359	94.0205	10.2755
0.0447	70.0	3500	0.2726	94.1025	91.0061	94.0359	94.0205	10.2755
0.0447	71.0	3550	0.2728	94.4023	91.4189	94.2894	94.2672	10.2143
0.0447	72.0	3600	0.2740	94.7457	91.7481	94.6594	94.6542	10.2857
0.0447	73.0	3650	0.2757	94.5402	91.4608	94.4489	94.4425	10.2959
0.0447	74.0	3700	0.2797	94.1724	91.0714	94.1084	94.067	10.2347
0.0447	75.0	3750	0.2778	94.1724	91.0714	94.1084	94.067	10.2347
0.0447	76.0	3800	0.2815	93.9926	90.8618	93.9248	93.9196	10.2551
0.0447	77.0	3850	0.2800	94.3659	91.3709	94.3159	94.2821	10.2245
0.0447	78.0	3900	0.2792	94.274	91.4729	94.22	94.1829	10.2041
0.0447	79.0	3950	0.2799	94.1724	91.0714	94.1084	94.067	10.2347
0.0396	80.0	4000	0.2836	94.155	90.916	94.0722	94.074	10.3367
0.0396	81.0	4050	0.2832	94.4269	91.5364	94.3413	94.3456	10.2857
0.0396	82.0	4100	0.2806	94.4269	91.5364	94.3413	94.3456	10.2857
0.0396	83.0	4150	0.2807	94.5193	91.8587	94.3946	94.3859	10.1837
0.0396	84.0	4200	0.2783	94.5193	91.8587	94.3946	94.3859	10.1837
0.0396	85.0	4250	0.2793	94.5193	91.8587	94.3946	94.3859	10.1837
0.0396	86.0	4300	0.2806	94.7428	92.1189	94.6022	94.6323	10.1735
0.0396	87.0	4350	0.2802	94.4023	91.4189	94.2894	94.2672	10.2143
0.0396	88.0	4400	0.2793	94.5193	91.8587	94.3946	94.3859	10.1837
0.0396	89.0	4450	0.2811	94.5193	91.8587	94.3946	94.3859	10.1837
0.0369	90.0	4500	0.2812	94.5193	91.8587	94.3946	94.3859	10.1837
0.0369	91.0	4550	0.2843	94.5402	91.4608	94.4489	94.4425	10.2959
0.0369	92.0	4600	0.2835	94.5193	91.8587	94.3946	94.3859	10.1837
0.0369	93.0	4650	0.2834	94.5193	91.8587	94.3946	94.3859	10.1837
0.0369	94.0	4700	0.2833	94.5193	91.8587	94.3946	94.3859	10.1837
0.0369	95.0	4750	0.2839	94.5193	91.8587	94.3946	94.3859	10.1837
0.0369	96.0	4800	0.2838	94.6487	91.9112	94.5291	94.5651	10.2653
0.0369	97.0	4850	0.2837	94.5193	91.8587	94.3946	94.3859	10.1837
0.0369	98.0	4900	0.2836	94.6487	91.9112	94.5291	94.5651	10.2653
0.0369	99.0	4950	0.2835	94.5193	91.8587	94.3946	94.3859	10.1837
0.0337	100.0	5000	0.2835	94.5193	91.8587	94.3946	94.3859	10.1837

Framework versions

Transformers 4.30.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.13.3

Library: transformers
Type: t5
License: apache-2.0
Created: Nov 18, 2023
Updated: Nov 20, 2023