ptt5-xlsumm-cstnews-1024

This model is a fine-tuned version of arthurmluz/ptt5-xlsumm-30epochs on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.2582
• Rouge1: 0.2812
• Rouge2: 0.213
• Rougel: 0.2582
• Rougelsum: 0.2747
• Gen Len: 19.0

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	47	1.4425	0.2155	0.1096	0.1749	0.2026	19.0
No log	2.0	94	1.3388	0.2385	0.1386	0.1902	0.2251	19.0
No log	3.0	141	1.2907	0.2491	0.1664	0.2162	0.2399	19.0
No log	4.0	188	1.2664	0.2588	0.1828	0.2282	0.2497	19.0
1.6936	5.0	235	1.2464	0.2636	0.1898	0.2363	0.256	19.0
1.6936	6.0	282	1.2430	0.2707	0.2005	0.2454	0.2645	19.0
1.6936	7.0	329	1.2320	0.2717	0.2023	0.2463	0.2654	19.0
1.6936	8.0	376	1.2276	0.2733	0.2054	0.2461	0.2692	19.0
1.2963	9.0	423	1.2237	0.2753	0.2114	0.2482	0.2701	19.0
1.2963	10.0	470	1.2225	0.2784	0.2143	0.2506	0.2732	19.0
1.2963	11.0	517	1.2247	0.2753	0.2117	0.2495	0.2702	19.0
1.2963	12.0	564	1.2244	0.2792	0.2137	0.2533	0.2738	19.0
1.1352	13.0	611	1.2285	0.2797	0.2125	0.2542	0.2742	19.0
1.1352	14.0	658	1.2287	0.2751	0.2096	0.2507	0.2684	19.0
1.1352	15.0	705	1.2325	0.2727	0.2089	0.2503	0.2672	19.0
1.1352	16.0	752	1.2330	0.2769	0.2143	0.2552	0.2711	19.0
1.1352	17.0	799	1.2353	0.2769	0.2143	0.2552	0.2711	19.0
1.0196	18.0	846	1.2352	0.2831	0.2176	0.261	0.2771	19.0
1.0196	19.0	893	1.2400	0.2838	0.2184	0.2611	0.2771	19.0
1.0196	20.0	940	1.2406	0.2838	0.2184	0.2611	0.2771	19.0
1.0196	21.0	987	1.2457	0.2771	0.2109	0.2554	0.2711	19.0
0.912	22.0	1034	1.2471	0.2771	0.2109	0.2554	0.2711	19.0
0.912	23.0	1081	1.2499	0.278	0.2094	0.2552	0.2714	19.0
0.912	24.0	1128	1.2508	0.278	0.2094	0.2552	0.2714	19.0
0.912	25.0	1175	1.2541	0.282	0.2139	0.2588	0.275	19.0
0.8667	26.0	1222	1.2563	0.282	0.2139	0.2588	0.275	19.0
0.8667	27.0	1269	1.2569	0.2812	0.213	0.2582	0.2747	19.0
0.8667	28.0	1316	1.2577	0.2812	0.213	0.2582	0.2747	19.0
0.8667	29.0	1363	1.2582	0.2812	0.213	0.2582	0.2747	19.0
0.8561	30.0	1410	1.2582	0.2812	0.213	0.2582	0.2747	19.0

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.14.1