bart-large-cnn-finetuned-roundup-64

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.4772
• Rouge1: 46.5444
• Rouge2: 27.4056
• Rougel: 29.6779
• Rougelsum: 44.0905
• Gen Len: 142.0

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: 64
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	132	1.3213	48.3389	28.6641	31.4086	45.6679	142.0
No log	2.0	264	1.2325	48.798	29.3068	31.4329	45.7945	142.0
No log	3.0	396	1.2791	47.1449	27.3965	30.56	44.4704	142.0
0.9574	4.0	528	1.3134	46.2319	25.6249	28.7673	43.7555	140.3
0.9574	5.0	660	1.3187	46.7313	25.3467	29.3873	43.9495	142.0
0.9574	6.0	792	1.4271	48.1638	27.8874	30.5334	45.9944	142.0
0.9574	7.0	924	1.4876	46.7481	25.7259	29.7214	43.7042	140.5
0.3303	8.0	1056	1.5259	46.7075	26.0716	29.5521	43.7312	142.0
0.3303	9.0	1188	1.6223	48.012	27.2795	30.4989	45.4644	142.0
0.3303	10.0	1320	1.6842	48.0074	26.8831	29.3396	45.1937	142.0
0.3303	11.0	1452	1.7317	46.52	26.5152	29.5124	43.8797	142.0
0.1478	12.0	1584	1.8087	47.5887	27.0488	29.8569	44.7318	140.8
0.1478	13.0	1716	1.8263	46.1251	25.8576	30.1698	42.7228	142.0
0.1478	14.0	1848	1.9459	46.4034	25.7039	28.2542	43.7254	142.0
0.1478	15.0	1980	1.9539	44.4666	24.5827	27.7147	41.9769	142.0
0.0779	16.0	2112	1.9654	47.2267	26.4562	29.7352	44.0823	142.0
0.0779	17.0	2244	1.9580	48.5086	28.0294	30.8311	45.6336	142.0
0.0779	18.0	2376	2.0065	48.293	28.5678	30.0243	45.1384	142.0
0.0499	19.0	2508	1.9313	49.0549	28.9695	32.0711	46.3834	142.0
0.0499	20.0	2640	2.0176	47.0121	25.1606	29.0108	44.1556	142.0
0.0499	21.0	2772	2.0711	48.3754	28.2221	30.772	45.8547	140.95
0.0499	22.0	2904	2.0848	45.7392	25.254	29.0833	43.0381	142.0
0.0335	23.0	3036	2.0711	47.2931	27.4573	30.718	44.5932	142.0
0.0335	24.0	3168	2.1200	50.515	30.4253	33.7045	47.6158	142.0
0.0335	25.0	3300	2.1097	46.4737	26.3055	29.0148	43.2135	142.0
0.0335	26.0	3432	2.1695	46.9099	26.5227	29.7757	44.0613	142.0
0.0249	27.0	3564	2.1494	47.8319	27.6364	31.3593	45.065	141.95
0.0249	28.0	3696	2.1510	47.504	26.8971	31.7196	45.0328	142.0
0.0249	29.0	3828	2.1612	46.8789	27.266	30.1009	43.8248	142.0
0.0249	30.0	3960	2.1579	47.7012	27.7761	30.935	44.3686	142.0
0.018	31.0	4092	2.1981	48.4703	29.167	31.9815	45.8005	142.0
0.018	32.0	4224	2.2332	45.9512	25.8111	29.2467	42.9234	142.0
0.018	33.0	4356	2.1944	47.7189	28.1413	30.9692	44.9361	142.0
0.018	34.0	4488	2.2589	50.9687	32.3987	36.5644	48.3938	142.0
0.0132	35.0	4620	2.2269	47.8241	28.0442	31.5535	44.9394	142.0
0.0132	36.0	4752	2.2865	47.4383	27.0825	30.4109	44.194	142.0
0.0132	37.0	4884	2.3267	49.1786	29.6416	32.875	46.8821	142.0
0.0095	38.0	5016	2.2872	48.2085	28.3304	32.1473	45.3571	142.0
0.0095	39.0	5148	2.3340	46.6762	26.1637	29.0149	43.5923	142.0
0.0095	40.0	5280	2.3425	46.7561	26.1645	29.6337	43.6188	142.0
0.0095	41.0	5412	2.3111	49.4118	29.9761	33.4765	46.601	142.0
0.0076	42.0	5544	2.3892	45.3335	25.0161	28.4124	41.9873	142.0
0.0076	43.0	5676	2.3808	46.2506	26.4283	29.3841	42.7488	142.0
0.0076	44.0	5808	2.3825	45.6823	26.0048	29.5501	42.6475	142.0
0.0076	45.0	5940	2.3592	47.9127	26.7924	30.2353	44.791	142.0
0.0051	46.0	6072	2.4206	46.0415	27.0681	29.9602	43.1225	142.0
0.0051	47.0	6204	2.4214	48.1229	29.0913	31.1828	45.0022	142.0
0.0051	48.0	6336	2.4176	47.3825	27.7622	30.4138	43.9047	142.0
0.0051	49.0	6468	2.4137	48.2544	28.277	31.5548	45.6053	142.0
0.0041	50.0	6600	2.4384	49.6459	30.186	33.0059	47.0483	142.0
0.0041	51.0	6732	2.4433	47.7279	27.7857	30.2982	45.0842	142.0
0.0041	52.0	6864	2.4068	48.6047	28.1758	31.2744	45.8336	142.0
0.0041	53.0	6996	2.4362	48.7095	29.3335	31.9509	46.4161	142.0
0.003	54.0	7128	2.4307	48.836	29.6069	32.4004	46.1986	142.0
0.003	55.0	7260	2.4292	47.2945	26.7577	28.9719	43.8988	142.0
0.003	56.0	7392	2.4425	45.2261	25.6879	28.8129	42.6474	142.0
0.0024	57.0	7524	2.4386	47.967	28.5415	32.2049	45.5111	142.0
0.0024	58.0	7656	2.4528	47.5552	27.6397	30.9151	44.2627	142.0
0.0024	59.0	7788	2.4574	46.7821	27.3368	30.6334	44.0533	142.0
0.0024	60.0	7920	2.4659	47.3507	26.8371	30.4566	44.4452	142.0
0.0018	61.0	8052	2.4766	47.9847	28.2678	30.0664	45.0071	142.0
0.0018	62.0	8184	2.4682	46.8392	27.1275	30.144	43.6379	142.0
0.0018	63.0	8316	2.4754	45.6338	26.2812	29.4831	42.8744	142.0
0.0018	64.0	8448	2.4772	46.5444	27.4056	29.6779	44.0905	142.0

Framework versions

• Transformers 4.18.0
• Pytorch 1.11.0+cu113
• Datasets 2.1.0
• Tokenizers 0.12.1