bart-large-cnn-small-xsum-5epochs – AI Model by bheshaj | AlphaNeural AI

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bart-large-cnn-small-xsum-5epochs

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bart-large-cnn-small-xsum-5epochs

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

Loss: 2.7051
Rouge1: 0.2859
Rouge2: 0.0937
Rougel: 0.2033
Rougelsum: 0.2101

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: 2.045e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 16
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.5007	0.32	16	2.0311	0.2393	0.0609	0.1618	0.1832
2.0942	0.64	32	1.9169	0.2906	0.1053	0.2072	0.2166
1.7543	0.96	48	1.9069	0.2904	0.0955	0.2058	0.2187
1.2476	1.28	64	1.9614	0.2928	0.1043	0.2081	0.2257
1.2318	1.6	80	1.9622	0.2892	0.0976	0.2099	0.2245
1.0768	1.92	96	2.0244	0.2935	0.1008	0.2095	0.2209
0.8845	2.24	112	2.0605	0.2886	0.0992	0.2039	0.2146
0.5722	2.56	128	2.2340	0.2852	0.0946	0.1983	0.2146
0.7132	2.88	144	2.1948	0.2838	0.0961	0.2047	0.2163
0.4438	3.2	160	2.3758	0.2869	0.0906	0.1987	0.2102
0.4194	3.52	176	2.5609	0.2882	0.0916	0.2022	0.2133
0.3404	3.84	192	2.4988	0.2884	0.0907	0.2022	0.213
0.2929	4.16	208	2.5802	0.2885	0.0967	0.2046	0.2141
0.2466	4.48	224	2.6590	0.2823	0.094	0.1994	0.2119
0.1889	4.8	240	2.7051	0.2859	0.0937	0.2033	0.2101

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu117
Datasets 2.7.1
Tokenizers 0.13.2