t5-large-finetuned2

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

• Loss: 0.0000
• Rouge1: 1.0
• Rouge2: 0.9378
• Rougel: 1.0
• Rougelsum: 1.0
• Gen Len: 5.9868

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: 0.0003
• train_batch_size: 8
• eval_batch_size: 8
• seed: 1
• 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
1.2513	1.0	1111	0.9524	0.1962	0.1068	0.1958	0.1958	4.722
1.0513	2.0	2222	0.7301	0.2556	0.1631	0.2544	0.2543	5.5469
0.839	3.0	3333	0.5738	0.3136	0.2165	0.312	0.3125	5.3629
0.7156	4.0	4444	0.4505	0.3808	0.2794	0.3797	0.38	5.5364
0.6135	5.0	5555	0.3600	0.4435	0.352	0.4425	0.4427	5.6558
0.5271	6.0	6666	0.2743	0.5288	0.4371	0.5279	0.5283	5.7094
0.439	7.0	7777	0.2246	0.5781	0.4842	0.5772	0.5776	5.6331
0.3821	8.0	8888	0.1728	0.6557	0.5675	0.6549	0.6551	5.8646
0.3297	9.0	9999	0.1379	0.7083	0.6211	0.7075	0.7076	5.8331
0.2805	10.0	11110	0.1067	0.769	0.6867	0.7684	0.7685	5.8528
0.2465	11.0	12221	0.0845	0.812	0.7324	0.8113	0.8115	5.918
0.2079	12.0	13332	0.0691	0.8516	0.7748	0.8515	0.8515	5.9435
0.1746	13.0	14443	0.0527	0.8785	0.8028	0.8784	0.8783	5.9311
0.1551	14.0	15554	0.0420	0.9123	0.8387	0.9123	0.9124	5.9516
0.1374	15.0	16665	0.0304	0.9368	0.8657	0.9367	0.9367	5.9531
0.1153	16.0	17776	0.0239	0.9501	0.8822	0.95	0.95	5.967
0.0821	17.0	18887	0.0204	0.9604	0.8935	0.9603	0.9603	5.9743
0.077	18.0	19998	0.0180	0.9722	0.9049	0.9721	0.9721	5.9863
0.0784	19.0	21109	0.0118	0.9813	0.9165	0.9812	0.9812	5.9845
0.0669	20.0	22220	0.0133	0.9796	0.9143	0.9796	0.9796	5.9817
0.0511	21.0	23331	0.0082	0.9878	0.9224	0.9877	0.9877	5.986
0.0524	22.0	24442	0.0079	0.9861	0.9212	0.9861	0.9861	5.9845
0.0397	23.0	25553	0.0060	0.9907	0.9272	0.9907	0.9907	5.9832
0.0284	24.0	26664	0.0060	0.9906	0.9267	0.9906	0.9906	5.985
0.0374	25.0	27775	0.0047	0.993	0.9289	0.9929	0.993	5.9905
0.0289	26.0	28886	0.0033	0.9944	0.9311	0.9944	0.9945	5.9909
0.0304	27.0	29997	0.0034	0.9947	0.931	0.9948	0.9948	5.9873
0.0232	28.0	31108	0.0036	0.9944	0.9312	0.9944	0.9944	5.9814
0.0208	29.0	32219	0.0030	0.996	0.9332	0.996	0.996	5.9882
0.0151	30.0	33330	0.0023	0.9963	0.9333	0.9963	0.9963	5.9813
0.0193	31.0	34441	0.0020	0.9965	0.9339	0.9964	0.9965	5.9869
0.0171	32.0	35552	0.0022	0.997	0.9338	0.997	0.997	5.9865
0.0124	33.0	36663	0.0015	0.9978	0.935	0.9979	0.9979	5.9842
0.0096	34.0	37774	0.0016	0.9984	0.9358	0.9984	0.9984	5.9853
0.0107	35.0	38885	0.0005	0.9988	0.9365	0.9989	0.9989	5.9901
0.009	36.0	39996	0.0011	0.999	0.9366	0.9989	0.9989	5.9887
0.01	37.0	41107	0.0008	0.9985	0.9365	0.9986	0.9986	5.9895
0.0049	38.0	42218	0.0010	0.9985	0.9361	0.9985	0.9985	5.9899
0.0072	39.0	43329	0.0004	0.9994	0.937	0.9994	0.9994	5.9866
0.0033	40.0	44440	0.0003	0.9996	0.9375	0.9996	0.9996	5.9884
0.0028	41.0	45551	0.0003	0.9996	0.9374	0.9996	0.9996	5.9887
0.0031	42.0	46662	0.0002	0.9998	0.9377	0.9998	0.9998	5.9856
0.0026	43.0	47773	0.0002	0.9996	0.9374	0.9996	0.9996	5.9869
0.0022	44.0	48884	0.0001	0.9999	0.9377	0.9999	0.9999	5.9868
0.0015	45.0	49995	0.0000	1.0	0.9378	1.0	1.0	5.9868
0.0014	46.0	51106	0.0000	1.0	0.9378	1.0	1.0	5.9868
0.0017	47.0	52217	0.0000	1.0	0.9378	1.0	1.0	5.9868
0.0018	48.0	53328	0.0000	1.0	0.9378	1.0	1.0	5.9868
0.0007	49.0	54439	0.0000	1.0	0.9378	1.0	1.0	5.9868
0.0015	50.0	55550	0.0000	1.0	0.9378	1.0	1.0	5.9868

Framework versions

• Transformers 4.34.1
• Pytorch 2.0.1
• Datasets 2.14.6
• Tokenizers 0.14.1