2870372f01e8e618f3a76eb129eba07c

This model is a fine-tuned version of albert/albert-large-v2 on the nyu-mll/glue dataset. It achieves the following results on the evaluation set:

• Loss: 1.1020
• Data Size: 0.5
• Epoch Runtime: 403.0306
• Accuracy: 0.3545
• F1 Macro: 0.1745
• Rouge1: 0.3544
• Rouge2: 0.0
• Rougel: 0.3545
• Rougelsum: 0.3543

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	1.1024	0	6.6083	0.3560	0.2434	0.3556	0.0	0.3560	0.3560
1.1303	1	12271	1.1021	0.0078	14.3098	0.3273	0.1644	0.3273	0.0	0.3275	0.3277
1.1167	2	24542	1.0998	0.0156	18.9131	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1251	3	36813	1.0974	0.0312	30.9038	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.104	4	49084	1.1000	0.0625	55.2274	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1019	5	61355	1.0982	0.125	104.1752	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.0985	6	73626	1.1044	0.25	202.5973	0.3182	0.1609	0.3184	0.0	0.3182	0.3183
1.1135	7	85897	1.1020	0.5	403.0306	0.3545	0.1745	0.3544	0.0	0.3545	0.3543

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.1