flan_merged1

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

• Loss: 0.1597
• Rouge1: 66.8856
• Rouge2: 55.6869
• Rougel: 63.8241
• Rougelsum: 66.7005
• Gen Len: 16.3392

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: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
11.8095	0.35	200	0.5275	38.2792	29.3331	37.9276	38.1283	8.0624
0.4481	0.7	400	0.3046	64.4437	52.3632	62.0225	64.2515	16.4262
0.3616	1.05	600	0.2656	64.9871	53.1185	62.4919	64.739	16.4279
0.2944	1.41	800	0.2412	65.2117	53.5512	62.6779	64.9318	16.4464
0.264	1.76	1000	0.2295	65.5748	54.0948	62.9803	65.3339	16.3866
0.2571	2.11	1200	0.2223	65.7216	53.793	62.9877	65.491	16.1898
0.2364	2.46	1400	0.2164	65.5444	53.9296	62.9975	65.3055	16.3172
0.2293	2.81	1600	0.2029	65.7977	54.3067	63.1851	65.5544	16.1766
0.2129	3.16	1800	0.2006	65.8342	53.9105	63.163	65.6175	16.1757
0.2184	3.51	2000	0.1931	65.1608	53.7707	62.6719	64.9743	16.1547
0.1952	3.87	2200	0.1873	66.3361	54.8382	63.2054	66.0954	16.3155
0.1992	4.22	2400	0.1847	66.316	55.0379	63.5154	66.0694	16.3594
0.1873	4.57	2600	0.1811	66.4999	55.263	63.8319	66.2513	16.3146
0.1839	4.92	2800	0.1783	66.0055	54.3406	62.9554	65.7387	16.3304
0.1748	5.27	3000	0.1777	66.1592	54.8048	63.407	66.0067	16.3348
0.1844	5.62	3200	0.1736	66.7642	55.3404	63.7069	66.5324	16.2996
0.1745	5.98	3400	0.1698	66.3946	55.1716	63.5596	66.1663	16.3216
0.1739	6.33	3600	0.1678	66.4472	55.1785	63.602	66.2704	16.3049
0.1633	6.68	3800	0.1680	66.6666	55.4584	63.8058	66.4708	16.3445
0.1659	7.03	4000	0.1682	66.6592	55.3712	63.5841	66.4587	16.2953
0.1557	7.38	4200	0.1634	66.876	55.423	63.8431	66.5569	16.2434
0.158	7.73	4400	0.1622	66.6165	55.2948	63.5996	66.4314	16.3849
0.1647	8.08	4600	0.1622	66.7592	55.5552	63.7194	66.5229	16.2794
0.1579	8.44	4800	0.1614	66.7889	55.5768	63.8266	66.5511	16.3181
0.1526	8.79	5000	0.1610	66.7516	55.5383	63.6509	66.5754	16.261
0.1506	9.14	5200	0.1608	66.9266	55.6277	63.7712	66.6668	16.3445
0.1502	9.49	5400	0.1604	66.9759	55.6586	63.8856	66.7849	16.3251
0.158	9.84	5600	0.1597	66.8856	55.6869	63.8241	66.7005	16.3392

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.14.0