led-large-16384-cnn_dailymail

Xmm/led-large-16384-cnn_dailymail

led-large-16384-cnn_dailymail is a machine learning model from Xmm. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: led
Access: Public
Created: Jun 17, 2023
Updated: Sep 2, 2023
SHA: f7394611

Datasets

cnn_dailymail

Model card

From the Hugging Face model README

led-large-16384-cnn_dailymail

This model is a fine-tuned version of allenai/led-base-16384 on the cnn_dailymail dataset. It achieves the following results on the evaluation set:

Loss: 1.5544
Rouge1: 0.3870
Rouge2: 0.1736
Rougel: 0.2599
Rougelsum: 0.3653

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 64
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.9531	0.4	500	1.8639	0.3485	0.1441	0.2275	0.3288
1.9563	0.8	1000	1.8260	0.3538	0.1482	0.2315	0.3343
1.7176	1.2	1500	1.8208	0.3628	0.1527	0.2383	0.3433
1.7197	1.6	2000	1.8162	0.3696	0.1602	0.2434	0.3486
1.8086	2.0	2500	1.7924	0.3558	0.1533	0.2334	0.3361
1.2448	2.4	3000	1.8510	0.3703	0.1591	0.2447	0.3483
1.3574	2.8	3500	1.8277	0.3741	0.1593	0.2422	0.3540
1.0966	3.2	4000	1.8924	0.3682	0.1576	0.2424	0.3479
0.9938	3.6	4500	1.8957	0.3723	0.1599	0.2451	0.3511
1.0735	4.0	5000	1.8772	0.3653	0.1557	0.2399	0.3454
0.9106	4.4	5500	1.9401	0.3720	0.1585	0.2436	0.3504
1.015	4.8	6000	1.9320	0.3725	0.1570	0.2429	0.3515
1.7854	0.36	6500	1.7800	0.3624	0.1544	0.2390	0.3422
1.9079	0.39	7000	1.7629	0.3573	0.1553	0.2352	0.3370
1.7606	3.34	7500	1.6902	0.3783	0.1673	0.2521	0.3570
1.7571	3.57	8000	1.6563	0.3802	0.1691	0.2538	0.3587
1.6602	3.79	8500	1.6439	0.3814	0.1693	0.2548	0.3600
1.6614	4.01	9000	1.6312	0.3812	0.1691	0.2544	0.3599
1.668	4.24	9500	1.6189	0.3815	0.1689	0.2550	0.3603
1.6491	4.46	10000	1.6172	0.3799	0.1681	0.2540	0.3586
1.5994	4.68	10500	1.6132	0.3825	0.1702	0.2560	0.3610
1.6493	4.9	11000	1.6093	0.3828	0.1701	0.2561	0.3613
1.6769	5.13	11500	1.6074	0.3831	0.1706	0.2569	0.3619
1.6554	5.35	12000	1.6044	0.3817	0.1695	0.2559	0.3605
1.6155	5.57	12500	1.6010	0.3825	0.1700	0.2561	0.3608
1.5863	5.8	13000	1.5981	0.3829	0.1704	0.2569	0.3614
1.6306	6.02	13500	1.6004	0.3831	0.1702	0.2563	0.3618
1.6425	6.24	14000	1.5987	0.3821	0.1698	0.2561	0.3610
1.6863	6.46	14500	1.5876	0.3837	0.1710	0.2569	0.3622
1.6085	6.69	15000	1.5815	0.3836	0.1717	0.2573	0.3621
1.6267	6.91	15500	1.5792	0.3852	0.1722	0.2579	0.3633
1.5637	7.13	16000	1.5768	0.3830	0.1709	0.2568	0.3611
1.5586	7.36	16500	1.5740	0.3833	0.1706	0.2567	0.3617
1.5389	7.58	17000	1.5689	0.3858	0.1729	0.2590	0.3640
1.5694	7.8	17500	1.5645	0.3853	0.1731	0.2589	0.3636
1.5265	8.02	18000	1.5621	0.3871	0.1733	0.2596	0.3654
1.5273	8.25	18500	1.5624	0.3861	0.1726	0.2588	0.3646
1.5148	8.47	19000	1.5602	0.3866	0.1733	0.2592	0.3651
1.532	8.69	19500	1.5599	0.3859	0.1732	0.2593	0.3642
1.5113	8.92	20000	1.5602	0.3877	0.1748	0.2606	0.3658
1.5133	9.14	20500	1.5595	0.3855	0.1725	0.2587	0.3637
1.4875	9.36	21000	1.5572	0.3873	0.1741	0.2600	0.3654
1.5038	9.59	21500	1.5557	0.3860	0.1728	0.2590	0.3641
1.5062	9.81	22000	1.5544	0.3870	0.1736	0.2599	0.3653

Framework versions

Transformers 4.27.1
Pytorch 2.0.0+cu118
Datasets 2.10.1
Tokenizers 0.13.2

Library: transformers
Type: led
License: apache-2.0
Created: Jun 17, 2023
Updated: Sep 2, 2023