4f51b512764d2ca833882b8d079cf6ca

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: 0.6429
• Data Size: 0.25
• Epoch Runtime: 3.3325
• Accuracy: 0.6651
• F1 Macro: 0.3994
• Rouge1: 0.6657
• Rouge2: 0.0
• Rougel: 0.6645
• Rougelsum: 0.6651

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	0.7160	0	1.5349	0.5507	0.4788	0.5501	0.0	0.5507	0.5507
No log	1	114	0.6592	0.0078	2.9093	0.6238	0.4800	0.6238	0.0	0.6232	0.6244
No log	2	228	0.6416	0.0156	1.7412	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	3	342	0.9583	0.0312	1.9291	0.6568	0.4313	0.6574	0.0	0.6562	0.6568
0.0225	4	456	0.6939	0.0625	2.2101	0.5713	0.5135	0.5719	0.0	0.5713	0.5719
0.0225	5	570	0.6812	0.125	2.5732	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0225	6	684	0.6429	0.25	3.3325	0.6651	0.3994	0.6657	0.0	0.6645	0.6651

Framework versions

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