9a17d48a8917a2a3692d699b3cff982d

This model is a fine-tuned version of studio-ousia/luke-japanese-base-lite on the nyu-mll/glue [mrpc] dataset. It achieves the following results on the evaluation set:

• Loss: 0.8079
• Data Size: 1.0
• Epoch Runtime: 13.2057
• Accuracy: 0.7748
• F1 Macro: 0.7211
• Rouge1: 0.7748
• Rouge2: 0.0
• Rougel: 0.7748
• Rougelsum: 0.7754

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: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 32
• total_eval_batch_size: 32
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Data Size	Epoch Runtime	Accuracy	F1 Macro	Rouge1	Rouge2	Rougel	Rougelsum
No log	0	0	0.8307	0	1.9456	0.3349	0.2509	0.3343	0.0	0.3355	0.3349
No log	1	114	0.6559	0.0078	2.8033	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	2	228	0.6814	0.0156	2.4253	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	3	342	0.6552	0.0312	2.7527	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0217	4	456	0.6671	0.0625	3.2082	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0217	5	570	0.6488	0.125	4.0516	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0217	6	684	0.5911	0.25	5.3531	0.7034	0.5882	0.7034	0.0	0.7034	0.7034
0.1507	7	798	0.5897	0.5	8.0809	0.6621	0.6597	0.6621	0.0	0.6621	0.6621
0.5375	8.0	912	0.5195	1.0	13.2007	0.7282	0.6035	0.7288	0.0	0.7276	0.7282
0.4721	9.0	1026	0.5217	1.0	13.0028	0.7482	0.6702	0.7482	0.0	0.7482	0.7488
0.3993	10.0	1140	0.5139	1.0	13.1375	0.7571	0.7283	0.7574	0.0	0.7577	0.7577
0.3642	11.0	1254	0.6024	1.0	13.2698	0.7594	0.6923	0.7594	0.0	0.7594	0.7588
0.2796	12.0	1368	0.6381	1.0	13.6686	0.7577	0.7149	0.7577	0.0	0.7577	0.7577
0.2594	13.0	1482	0.6610	1.0	13.2413	0.7594	0.7414	0.7600	0.0	0.7594	0.7594
0.2698	14.0	1596	0.8079	1.0	13.2057	0.7748	0.7211	0.7748	0.0	0.7748	0.7754

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.1