outputs

nishita/outputs

outputs is a machine learning model from nishita. 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: t5
Access: Public
Created: Aug 20, 2022
Updated: Aug 20, 2022
SHA: 27bbd335

Model card

From the Hugging Face model README

outputs

This model is a fine-tuned version of mrm8488/t5-base-finetuned-common_gen on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.2833
Rouge1: 79.0721
Rouge2: 59.355
Rougel: 70.9787
Rougelsum: 70.9177
Gen Len: 16.3819

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: 32
eval_batch_size: 32
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	241	2.3934	75.6585	53.2221	65.7693	65.7796	16.4214
No log	2.0	482	2.4223	76.75	55.466	67.3377	67.3425	16.4381
0.5668	3.0	723	2.5147	77.5314	56.5654	68.4518	68.4263	16.4235
0.5668	4.0	964	2.5164	78.3267	57.4609	69.1759	69.1835	16.4381
0.4546	5.0	1205	2.6058	78.6699	58.2725	69.8265	69.8802	16.358
0.4546	6.0	1446	2.6295	78.8943	58.182	69.5933	69.619	16.4464
0.4014	7.0	1687	2.6592	79.0497	58.7135	70.2046	70.2047	16.4152
0.4014	8.0	1928	2.6610	79.1756	59.143	70.5631	70.5854	16.4235
0.3652	9.0	2169	2.6636	79.2156	59.3209	70.5418	70.5757	16.4308
0.3652	10.0	2410	2.6647	78.9922	59.0252	70.49	70.5262	16.3569
0.341	11.0	2651	2.7105	79.0372	58.7891	70.2666	70.3333	16.4131
0.341	12.0	2892	2.7829	79.2323	59.4941	70.6877	70.7283	16.3684
0.3199	13.0	3133	2.7344	79.0326	58.8869	70.2648	70.2983	16.4329
0.3199	14.0	3374	2.7568	79.172	59.1695	70.5229	70.5505	16.435
0.301	15.0	3615	2.7773	78.9152	58.8776	70.368	70.3922	16.3923
0.301	16.0	3856	2.8178	79.1939	59.1455	70.6938	70.7153	16.4693
0.2883	17.0	4097	2.8105	79.1307	59.114	70.6573	70.67	16.4287
0.2883	18.0	4338	2.8360	78.8425	58.6959	70.4532	70.3955	16.358
0.2779	19.0	4579	2.8606	79.3713	59.3462	70.6531	70.648	16.3632
0.2779	20.0	4820	2.8513	79.0083	58.817	70.5779	70.5775	16.435
0.2664	21.0	5061	2.8882	79.1277	59.1771	70.8215	70.7963	16.4194
0.2664	22.0	5302	2.8852	79.0191	59.0226	70.6604	70.6499	16.3715
0.258	23.0	5543	2.9253	79.3219	59.1689	70.8438	70.8462	16.41
0.258	24.0	5784	2.9020	79.1941	59.044	70.6849	70.6449	16.3996
0.2494	25.0	6025	2.9579	79.5266	59.8762	71.5284	71.5085	16.4422
0.2494	26.0	6266	2.9660	79.1653	59.3299	70.7888	70.7755	16.4475
0.2415	27.0	6507	2.9682	79.3267	59.7158	71.1062	71.0864	16.411
0.2415	28.0	6748	3.0086	79.0646	59.1926	70.8172	70.78	16.4422
0.2415	29.0	6989	3.0190	78.9127	58.8431	70.4618	70.4597	16.4308
0.2361	30.0	7230	2.9958	79.1999	59.5494	70.9411	70.9	16.3684
0.2361	31.0	7471	2.9824	79.0219	59.0778	70.6465	70.6445	16.3809
0.2295	32.0	7712	3.0079	79.1633	59.4146	71.0352	70.9144	16.4017
0.2295	33.0	7953	2.9894	79.106	59.0905	70.644	70.622	16.4069
0.2247	34.0	8194	3.0256	78.8789	59.0956	70.8588	70.796	16.3954
0.2247	35.0	8435	3.0451	78.7977	58.889	70.6925	70.6408	16.3975
0.2196	36.0	8676	3.0475	78.8942	58.8944	70.4803	70.4858	16.3871
0.2196	37.0	8917	3.0289	78.7668	58.7055	70.3606	70.3058	16.411
0.2157	38.0	9158	3.0748	78.833	58.6404	70.271	70.2338	16.4266
0.2157	39.0	9399	3.0665	79.2216	59.3481	70.9647	70.9076	16.4058
0.2104	40.0	9640	3.0773	78.9717	58.8784	70.6392	70.6359	16.3704
0.2104	41.0	9881	3.0823	78.901	58.9511	70.737	70.7051	16.3788
0.2061	42.0	10122	3.0637	78.8689	58.8098	70.754	70.7783	16.36
0.2061	43.0	10363	3.0965	79.4409	59.5757	71.2304	71.2553	16.4422
0.2027	44.0	10604	3.1178	78.8964	58.9388	70.5416	70.5367	16.4048
0.2027	45.0	10845	3.1078	79.2306	59.5613	71.1202	71.0741	16.4089
0.1998	46.0	11086	3.1432	79.2393	59.5587	71.1138	71.0502	16.4485
0.1998	47.0	11327	3.1493	79.3553	59.5023	71.0459	71.0508	16.4256
0.1966	48.0	11568	3.1670	79.0395	59.1524	70.9374	70.921	16.4006
0.1966	49.0	11809	3.1580	79.2359	59.2038	71.0061	70.9725	16.4568
0.1941	50.0	12050	3.1458	79.1914	59.4751	71.2448	71.2131	16.437
0.1941	51.0	12291	3.1379	79.2891	59.6776	71.3416	71.2787	16.4287
0.1903	52.0	12532	3.1618	79.4208	59.7335	71.4722	71.4196	16.4173
0.1903	53.0	12773	3.1712	79.2976	59.736	71.1151	71.0491	16.4454
0.188	54.0	13014	3.1795	79.2538	59.2768	71.0137	70.9602	16.4183
0.188	55.0	13255	3.1836	79.4504	59.3201	71.0908	71.0614	16.4277
0.188	56.0	13496	3.1800	79.067	59.1309	70.7069	70.6896	16.3996
0.1858	57.0	13737	3.1660	79.2369	59.5092	71.0077	70.9537	16.3871
0.1858	58.0	13978	3.1886	79.0404	58.8652	70.6484	70.6162	16.3798
0.1849	59.0	14219	3.1413	79.1468	59.1964	70.8362	70.8076	16.4121
0.1849	60.0	14460	3.1778	79.0189	58.9894	70.597	70.5447	16.3788
0.1824	61.0	14701	3.1674	78.8303	58.7354	70.4517	70.4009	16.3871
0.1824	62.0	14942	3.1756	78.8914	58.7551	70.3439	70.3031	16.3965
0.1793	63.0	15183	3.1943	78.9093	58.8599	70.415	70.3998	16.4152
0.1793	64.0	15424	3.1970	78.8353	58.8096	70.4231	70.3813	16.4194
0.1768	65.0	15665	3.2301	79.0958	59.2143	70.837	70.7973	16.4162
0.1768	66.0	15906	3.2415	79.1112	59.1872	70.7534	70.7145	16.4173
0.177	67.0	16147	3.2332	79.0807	59.3005	70.6878	70.6351	16.3757
0.177	68.0	16388	3.2415	79.0587	59.1872	70.6691	70.6295	16.3819
0.1744	69.0	16629	3.2454	79.3829	59.5478	70.997	70.9835	16.4037
0.1744	70.0	16870	3.2239	79.1289	59.1661	70.7455	70.684	16.4412
0.1715	71.0	17111	3.2056	79.2074	59.1811	70.7366	70.682	16.4339
0.1715	72.0	17352	3.2101	79.1756	59.1883	70.8208	70.821	16.4079
0.1722	73.0	17593	3.2086	79.4652	59.7631	71.3223	71.2917	16.4214
0.1722	74.0	17834	3.2269	79.0154	59.0411	70.6754	70.6184	16.3621
0.17	75.0	18075	3.2389	79.1212	59.3143	70.7629	70.6935	16.3444
0.17	76.0	18316	3.2387	79.1148	59.4001	70.897	70.8525	16.3663
0.1682	77.0	18557	3.2505	79.0955	59.2033	70.8455	70.8303	16.3632
0.1682	78.0	18798	3.2670	79.1781	59.1849	70.8704	70.8286	16.3611
0.1686	79.0	19039	3.2825	79.0246	59.1434	70.7866	70.7353	16.3559
0.1686	80.0	19280	3.2816	79.0431	58.9944	70.6819	70.6257	16.3486
0.1664	81.0	19521	3.2567	79.0287	59.0805	70.7245	70.6889	16.3392
0.1664	82.0	19762	3.2750	78.9783	59.1123	70.6961	70.6369	16.3465
0.1659	83.0	20003	3.2757	78.9774	59.2248	70.7978	70.7407	16.3455
0.1659	84.0	20244	3.2800	79.0205	59.2371	70.8016	70.7532	16.3704
0.1659	85.0	20485	3.2723	78.9507	59.1004	70.6419	70.5825	16.3663
0.165	86.0	20726	3.2723	78.9698	59.1362	70.7738	70.7018	16.3652
0.165	87.0	20967	3.2740	79.0156	59.2383	70.7799	70.7075	16.384
0.1637	88.0	21208	3.2628	79.0806	59.3468	70.8158	70.753	16.3725
0.1637	89.0	21449	3.2605	78.9712	59.223	70.7653	70.7021	16.3621
0.1642	90.0	21690	3.2738	79.0793	59.3309	70.8753	70.8282	16.385
0.1642	91.0	21931	3.2674	78.9877	59.1468	70.7031	70.6598	16.3694
0.1636	92.0	22172	3.2706	79.006	59.1366	70.7069	70.6638	16.3757
0.1636	93.0	22413	3.2783	79.0874	59.2523	70.7881	70.7397	16.3746
0.1629	94.0	22654	3.2801	79.1126	59.2826	70.8352	70.7865	16.3715
0.1629	95.0	22895	3.2797	79.0586	59.3089	70.8126	70.7697	16.3757
0.1625	96.0	23136	3.2826	79.1019	59.3294	70.8399	70.803	16.3777
0.1625	97.0	23377	3.2844	79.0739	59.3167	70.9062	70.846	16.3767
0.1622	98.0	23618	3.2826	79.0739	59.3167	70.9062	70.846	16.3767
0.1622	99.0	23859	3.2830	79.0721	59.355	70.9787	70.9177	16.3819
0.161	100.0	24100	3.2833	79.0721	59.355	70.9787	70.9177	16.3819

Framework versions

Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1

Library: transformers
Type: t5
Created: Aug 20, 2022
Updated: Aug 20, 2022