12d1754f9387956ab4c04e06f4f04c9f

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

• Loss: 0.6583
• Data Size: 1.0
• Epoch Runtime: 1023.2721
• Accuracy: 0.6320
• F1 Macro: 0.3872
• Rouge1: 0.6318
• Rouge2: 0.0
• Rougel: 0.6319
• Rougelsum: 0.6317

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	0.6585	0	34.1334	0.6320	0.3872	0.6318	0.0	0.6319	0.6317
0.6518	1	11370	0.5219	0.0078	42.4415	0.7225	0.6904	0.7226	0.0	0.7225	0.7224
0.5324	2	22740	0.5142	0.0156	48.2432	0.7410	0.7083	0.7412	0.0	0.7411	0.7409
0.496	3	34110	0.4727	0.0312	63.1328	0.7564	0.7510	0.7566	0.0	0.7564	0.7563
0.4957	4	45480	0.4385	0.0625	92.6967	0.7846	0.7741	0.7846	0.0	0.7845	0.7846
0.5043	5	56850	0.6300	0.125	151.0473	0.7117	0.7116	0.7117	0.0	0.7116	0.7116
0.452	6	68220	0.4376	0.25	269.5394	0.7921	0.7842	0.7922	0.0	0.7921	0.7921
0.6572	7	79590	0.6581	0.5	518.4561	0.6320	0.3872	0.6318	0.0	0.6319	0.6317
0.6544	8.0	90960	0.6581	1.0	1030.7410	0.6320	0.3872	0.6318	0.0	0.6319	0.6317
0.6529	9.0	102330	0.6614	1.0	1026.0289	0.6320	0.3872	0.6318	0.0	0.6319	0.6317
0.6631	10.0	113700	0.6583	1.0	1023.2721	0.6320	0.3872	0.6318	0.0	0.6319	0.6317

Framework versions

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