bart_samsum_v2

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

• Loss: 0.0236

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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 8
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss
9.4233	0.17	1	9.1990
9.5213	0.34	2	8.5394
8.7467	0.52	3	8.1115
8.4697	0.69	4	7.5747
7.752	0.86	5	6.8712
7.0515	1.03	6	5.8670
6.0874	1.2	7	4.6814
5.0408	1.38	8	3.8055
4.14	1.55	9	2.6678
2.9893	1.72	10	1.9701
2.4337	1.89	11	1.5191
1.9451	2.06	12	1.2105
1.53	2.24	13	0.9714
1.2369	2.41	14	0.7905
1.0014	2.58	15	0.6478
0.8419	2.75	16	0.5493
0.7338	2.92	17	0.4770
0.6393	3.1	18	0.4151
0.5747	3.27	19	0.3691
0.4962	3.44	20	0.3293
0.4516	3.61	21	0.2935
0.3995	3.78	22	0.2614
0.3618	3.96	23	0.2346
0.3246	4.13	24	0.2129
0.2929	4.3	25	0.1938
0.278	4.47	26	0.1770
0.2493	4.65	27	0.1627
0.2273	4.82	28	0.1500
0.2067	4.99	29	0.1381
0.1917	5.16	30	0.1274
0.1805	5.33	31	0.1174
0.1557	5.51	32	0.1081
0.1495	5.68	33	0.1002
0.1394	5.85	34	0.0933
0.1261	6.02	35	0.0868
0.1155	6.19	36	0.0809
0.1114	6.37	37	0.0755
0.1041	6.54	38	0.0705
0.0952	6.71	39	0.0657
0.0881	6.88	40	0.0615
0.0823	7.05	41	0.0577
0.0778	7.23	42	0.0545
0.071	7.4	43	0.0515
0.07	7.57	44	0.0487
0.0625	7.74	45	0.0463
0.0589	7.91	46	0.0440
0.0567	8.09	47	0.0422
0.0537	8.26	48	0.0411
0.05	8.43	49	0.0398
0.0472	8.6	50	0.0384
0.0458	8.77	51	0.0363
0.0455	8.95	52	0.0347
0.0412	9.12	53	0.0340
0.0414	9.29	54	0.0326
0.0403	9.46	55	0.0333
0.0384	9.63	56	0.0303
0.0353	9.81	57	0.0298
0.0348	9.98	58	0.0293
0.0342	10.15	59	0.0275
0.0311	10.32	60	0.0272
0.0317	10.49	61	0.0270
0.0315	10.67	62	0.0261
0.0289	10.84	63	0.0253
0.0285	11.01	64	0.0247
0.0273	11.18	65	0.0244
0.0277	11.35	66	0.0240
0.0267	11.53	67	0.0237
0.0263	11.7	68	0.0237
0.0258	11.87	69	0.0237
0.0254	12.04	70	0.0238
0.0248	12.22	71	0.0239
0.0246	12.39	72	0.0239
0.0249	12.56	73	0.0237
0.0239	12.73	74	0.0236
0.0247	12.9	75	0.0236

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2