cnn_dailymail-summarization-t5-small-2022-09-05

This model is a fine-tuned version of t5-small on the cnn_dailymail 3.0.0 dataset. It achieves the following results on the evaluation set:

• Loss: 1.6455
• Rouge1: 41.4235
• Rouge2: 19.0263
• Rougel: 29.2892
• Rougelsum: 38.6338
• Gen Len: 73.7273

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: 5e-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: 3.0

Training results

Training Loss	Epoch	Step	Gen Len	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.9623	0.03	1000	18.9996	1.7500	24.1039	11.368	19.813	22.671
1.8827	0.06	2000	18.9993	1.7382	24.1445	11.4497	19.8683	22.7376
1.8988	0.08	3000	18.9999	1.7310	24.329	11.5899	20.0409	22.9104
1.8778	0.11	4000	19.0	1.7177	24.3886	11.6472	20.1048	22.988
1.9173	0.14	5000	18.9996	1.7140	24.3508	11.5594	20.075	22.932
1.9009	0.17	6000	18.9995	1.7134	24.28	11.6075	20.0581	22.8833
1.8975	0.2	7000	18.9994	1.7081	24.3203	11.6175	20.035	22.9167
1.8835	0.22	8000	18.9996	1.7061	24.2729	11.6324	20.0728	22.8747
1.8725	0.25	9000	19.0	1.6995	24.2542	11.5763	20.0241	22.8713
1.837	0.28	10000	18.9997	1.6998	24.3321	11.599	20.1028	22.9562
1.8629	0.31	11000	19.0	1.6944	24.4161	11.6208	20.1374	23.024
1.85	0.33	12000	18.9990	1.7002	24.3514	11.6883	20.134	22.9515
1.8506	0.36	13000	18.9987	1.6894	24.3812	11.6592	20.1641	23.0108
1.8869	0.39	14000	18.9995	1.6881	24.3956	11.6817	20.1654	23.0284
1.8327	0.42	15000	18.9993	1.6903	24.3707	11.6446	20.1353	22.9801
1.8204	0.45	16000	18.9993	1.6896	24.3663	11.6963	20.1357	22.9898
1.8764	0.47	17000	18.9978	1.6846	24.4212	11.652	20.1455	23.0326
1.8213	0.5	18000	18.9992	1.6817	24.452	11.7014	20.1898	23.0668
1.8424	0.53	19000	18.9990	1.6844	24.4206	11.7049	20.1931	23.0358
1.8721	0.56	20000	18.9996	1.6814	24.4483	11.6789	20.1798	23.0508
1.87	0.59	21000	18.9996	1.6796	24.4799	11.6789	20.1919	23.0831
1.844	0.61	22000	18.9996	1.6770	24.4741	11.7433	20.2031	23.0535
1.8611	0.64	23000	18.9986	1.6785	24.4837	11.7572	20.219	23.088
1.8201	0.67	24000	18.9993	1.6796	24.3955	11.6978	20.173	23.0302
1.8506	0.7	25000	18.9995	1.6770	24.4084	11.711	20.1851	23.0266
1.846	0.72	26000	18.9990	1.6765	24.4272	11.6779	20.1785	23.0352
1.8431	0.75	27000	18.9998	1.6757	24.4484	11.7154	20.2156	23.0646
1.8208	0.78	28000	18.9993	1.6764	24.412	11.6887	20.1752	23.0151
1.8108	0.81	29000	18.9997	1.6733	24.4051	11.7155	20.1773	23.0215
1.847	0.84	30000	18.9994	1.6738	24.5531	11.7949	20.2834	23.1588
1.8386	0.86	31000	18.9991	1.6674	24.5155	11.7333	20.2529	23.145
1.82	0.89	32000	18.9988	1.6693	24.4498	11.7118	20.2183	23.0767
1.8475	0.92	33000	18.9993	1.6676	24.442	11.676	20.168	23.0409
1.7948	0.95	34000	18.9990	1.6689	24.4561	11.7865	20.2446	23.0707
1.8357	0.98	35000	18.9994	1.6757	24.4005	11.7299	20.1999	23.0093
1.8624	1.0	36000	18.9988	1.6745	24.3371	11.6749	20.1257	22.9428
1.8309	1.03	37000	18.9995	1.6675	24.5108	11.8038	20.2691	23.117
1.8237	1.06	38000	18.9996	1.6654	24.482	11.7485	20.2225	23.0917
1.7743	1.09	39000	18.9993	1.6681	24.5106	11.7511	20.2583	23.123
1.7811	1.11	40000	18.9991	1.6636	24.6194	11.843	20.3375	23.2259
1.7973	1.14	41000	19.0	1.6666	24.5434	11.8133	20.3033	23.165
1.8156	1.17	42000	18.9993	1.6660	24.4857	11.7526	20.2406	23.1081
1.8403	1.2	43000	18.9998	1.6621	24.4632	11.7525	20.2459	23.0692
1.8129	1.23	44000	18.9999	1.6643	24.6032	11.8251	20.3368	23.1806
1.7896	1.25	45000	18.9993	1.6622	24.4619	11.7769	20.2516	23.0647
1.7948	1.28	46000	18.9992	1.6608	24.5468	11.8041	20.2941	23.1551
1.8043	1.31	47000	18.9993	1.6614	24.5774	11.8246	20.3189	23.1836
1.7884	1.34	48000	18.9993	1.6581	24.5688	11.843	20.2993	23.1756
1.8041	1.37	49000	18.9996	1.6614	24.5454	11.8346	20.3179	23.1605
1.8192	1.39	50000	18.9998	1.6597	24.5017	11.7755	20.2439	23.1148
1.8679	1.42	51000	18.9995	1.6555	24.5302	11.7638	20.2592	23.1395
1.82	1.45	52000	18.9998	1.6571	24.546	11.7798	20.265	23.1408
1.8267	1.48	53000	18.9996	1.6552	24.5214	11.7368	20.2276	23.1504
1.8063	1.5	54000	18.9992	1.6588	24.5222	11.8209	20.2941	23.1551
1.8171	1.53	55000	18.9996	1.6569	24.5845	11.8182	20.3147	23.1812
1.7884	1.56	56000	18.9998	1.6597	24.532	11.8057	20.2622	23.1459
1.7588	1.59	57000	18.9994	1.6572	24.6532	11.8958	20.3877	23.2776
1.7847	1.62	58000	18.9996	1.6561	24.5483	11.856	20.3188	23.1852
1.8523	1.64	59000	18.9996	1.6584	24.5501	11.8666	20.3197	23.1683
1.7955	1.67	60000	18.9999	1.6546	24.5126	11.8043	20.2603	23.1175
1.8215	1.7	61000	18.9996	1.6541	24.5884	11.8003	20.2887	23.1866
1.7917	1.73	62000	18.9997	1.6568	24.619	11.8868	20.3496	23.2304
1.7543	1.76	63000	18.9996	1.6570	24.5378	11.8192	20.2681	23.1454
1.7978	1.78	64000	18.9999	1.6541	24.5719	11.8446	20.2873	23.1855
1.8228	1.81	65000	18.9998	1.6561	24.5193	11.8527	20.3185	23.1395
1.8163	1.84	66000	18.9998	1.6537	24.4385	11.7625	20.2042	23.0671
1.7868	1.87	67000	18.9998	1.6532	24.4985	11.8187	20.2775	23.1426
1.8345	1.89	68000	18.9999	1.6522	24.5375	11.8398	20.285	23.1643
1.7773	1.92	69000	18.9999	1.6529	24.4722	11.7979	20.2636	23.106
1.8409	1.95	70000	18.9999	1.6521	24.4845	11.8136	20.2557	23.1089
1.8146	1.98	71000	18.9999	1.6515	24.4923	11.7965	20.2521	23.1247
1.7466	2.01	72000	19.0	1.6526	24.4913	11.8254	20.2562	23.1266
1.8009	2.03	73000	19.0	1.6505	24.5231	11.8414	20.2842	23.1654
1.7768	2.06	74000	19.0	1.6516	24.5192	11.8206	20.2884	23.1493
1.7569	2.09	75000	19.0	1.6541	24.6135	11.9135	20.3513	23.2279
1.7893	2.12	76000	18.9997	1.6507	24.5934	11.8727	20.3305	23.2106
1.763	2.15	77000	18.9999	1.6512	24.5829	11.8543	20.3142	23.2049
1.7552	2.17	78000	18.9998	1.6506	24.5332	11.8309	20.2795	23.1654
1.7632	2.2	79000	18.9995	1.6498	24.5569	11.8313	20.3158	23.1808
1.8056	2.23	80000	18.9996	1.6488	24.6217	11.8877	20.3555	23.2514
1.8066	2.26	81000	18.9996	1.6494	24.5799	11.8515	20.3307	23.2059
1.7903	2.28	82000	18.9998	1.6487	24.6151	11.889	20.3739	23.2226
1.805	2.31	83000	18.9996	1.6493	24.5739	11.8659	20.3354	23.1884
1.7843	2.34	84000	18.9996	1.6487	24.6125	11.8879	20.3648	23.2274
1.8153	2.37	85000	18.9996	1.6493	24.5638	11.8392	20.3084	23.165
1.7581	2.4	86000	18.9996	1.6490	24.6121	11.8876	20.36	23.2163
1.6925	2.42	87000	18.9998	1.6502	24.6192	11.8992	20.3786	23.2421
1.7535	2.45	88000	18.9996	1.6473	24.6134	11.8877	20.3663	23.2262
1.751	2.48	89000	18.9996	1.6496	24.5728	11.8886	20.3411	23.1906
1.7577	2.51	90000	18.9996	1.6477	24.5616	11.8489	20.3021	23.1754
1.8	2.54	91000	18.9996	1.6473	24.5614	11.8663	20.3282	23.1868
1.7859	2.56	92000	18.9998	1.6483	24.5594	11.8426	20.3197	23.191
1.7984	2.59	93000	18.9998	1.6469	24.5732	11.8258	20.3204	23.1958
1.7943	2.62	94000	18.9996	1.6477	24.5888	11.8602	20.3352	23.2181
1.7888	2.65	95000	18.9996	1.6472	24.5781	11.844	20.3272	23.216
1.7803	2.67	96000	1.6483	24.5454	11.8245	20.2917	23.1727	18.9996
1.8106	2.7	97000	1.6461	24.5694	11.8344	20.3123	23.1934	18.9996
1.8713	2.73	98000	1.6454	24.5906	11.8573	20.3447	23.2181	18.9996
1.7655	2.76	99000	1.6468	24.5709	11.8573	20.3139	23.1994	18.9996
1.7616	2.79	100000	1.6464	24.5852	11.8531	20.3172	23.2089	18.9998
1.7581	2.81	101000	1.6468	24.5748	11.8452	20.3043	23.1849	18.9997
1.7743	2.84	102000	1.6462	24.5665	11.8328	20.2992	23.1896	18.9996
1.78	2.87	103000	1.6458	24.5716	11.8399	20.31	23.1943	18.9996
1.8162	2.9	104000	1.6456	24.5719	11.8358	20.3132	23.1921	18.9996
1.7862	2.93	105000	1.6462	24.5938	11.8624	20.337	23.2131	18.9996
1.7995	2.95	106000	1.6459	24.5885	11.8606	20.3325	23.2137	18.9996
1.7559	2.98	107000	1.6454	24.593	11.861	20.3401	23.2188	18.9996

Framework versions

• Transformers 4.22.0.dev0
• Pytorch 1.12.1+cu102
• Datasets 2.4.0
• Tokenizers 0.12.1