bart-large-cnn-finetuned-small-context-news-1000

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: 0.9930
• Rouge1: 65.1207
• Rouge2: 55.5654
• Rougel: 60.1703
• Rougelsum: 61.6717
• Gen Len: 66.6529

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	85	0.4915	61.0185	47.1863	53.5499	55.4476	66.2824
No log	2.0	170	0.5558	63.1675	51.7011	57.0742	58.1801	67.2235
No log	3.0	255	0.5447	64.6201	54.8904	59.8669	60.7456	67.4529
No log	4.0	340	0.5770	65.2542	54.571	59.89	61.0988	65.0941
No log	5.0	425	0.6406	64.8868	54.2641	59.2758	60.4861	67.4118
0.2062	6.0	510	0.6468	65.1216	54.5784	59.3594	60.3826	66.7529
0.2062	7.0	595	0.6828	64.162	54.1786	59.1392	60.2517	67.4412
0.2062	8.0	680	0.7481	64.6093	54.4423	59.9194	61.1767	66.2647
0.2062	9.0	765	0.7916	65.0347	55.2975	60.3007	61.4619	67.8471
0.2062	10.0	850	0.7699	65.672	55.5276	60.3711	61.5138	66.9529
0.2062	11.0	935	0.7712	65.7327	55.9363	61.0215	62.1639	65.7294
0.0273	12.0	1020	0.9920	65.2328	55.3817	60.0671	61.4812	66.3588
0.0273	13.0	1105	0.8023	65.2372	55.2458	60.2251	61.5193	65.4824
0.0273	14.0	1190	0.8660	65.0369	55.2548	59.8089	61.3785	68.0353
0.0273	15.0	1275	0.9539	65.4251	55.1068	60.2355	61.6598	66.7765
0.0273	16.0	1360	0.8840	65.544	55.951	59.9112	61.6029	66.7529
0.0273	17.0	1445	0.9141	65.7685	55.4981	60.575	62.2381	66.4882
0.009	18.0	1530	1.0024	65.4152	55.7546	60.5256	62.0985	67.2412
0.009	19.0	1615	0.9997	65.0153	55.1772	60.103	61.4286	66.3529
0.009	20.0	1700	0.9930	65.1207	55.5654	60.1703	61.6717	66.6529

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.2