3a753398fb7b0d2d006f7b1110696848

This model is a fine-tuned version of studio-ousia/luke-large-lite on the nyu-mll/glue [sst2] dataset. It achieves the following results on the evaluation set:

• Loss: 0.7495
• Data Size: 0.125
• Epoch Runtime: 49.3711
• Accuracy: 0.5093
• F1 Macro: 0.3374
• Rouge1: 0.5093
• Rouge2: 0.0
• Rougel: 0.5093
• Rougelsum: 0.5081

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	0.6949	0	1.9342	0.5093	0.3374	0.5093	0.0	0.5093	0.5081
No log	1	2104	0.6922	0.0078	5.3496	0.5278	0.4085	0.5284	0.0	0.5278	0.5266
No log	2	4208	0.6987	0.0156	8.0837	0.5093	0.3374	0.5093	0.0	0.5093	0.5081
0.015	3	6312	0.7081	0.0312	14.0883	0.5093	0.3374	0.5093	0.0	0.5093	0.5081
0.701	4	8416	0.6989	0.0625	25.6884	0.5093	0.3374	0.5093	0.0	0.5093	0.5081
0.6946	5	10520	0.7495	0.125	49.3711	0.5093	0.3374	0.5093	0.0	0.5093	0.5081

Framework versions

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