0e64222419bc669870e1e8d65290d2a8

This model is a fine-tuned version of albert/albert-xlarge-v1 on the nyu-mll/glue dataset. It achieves the following results on the evaluation set:

• Loss: 1.0991
• Data Size: 0.5
• Epoch Runtime: 364.8162
• 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	1.1462	0	5.8567	0.3327	0.2554	0.3325	0.0	0.3327	0.3327
1.1812	1	12271	1.1240	0.0078	11.6823	0.3182	0.1609	0.3184	0.0	0.3182	0.3183
1.1129	2	24542	1.0999	0.0156	16.8792	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.109	3	36813	1.0965	0.0312	28.1078	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1024	4	49084	1.0966	0.0625	50.0514	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1014	5	61355	1.0976	0.125	94.0639	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.099	6	73626	1.1042	0.25	182.1330	0.3182	0.1609	0.3184	0.0	0.3182	0.3183
1.0996	7	85897	1.0991	0.5	364.8162	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