b234dd03f4c1ae921b5928dffb99cc90

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: 0.6414
• Data Size: 0.25
• Epoch Runtime: 3.7161
• 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	0.7695	0	1.7141	0.3379	0.2604	0.3379	0.0	0.3384	0.3379
No log	1	114	0.6448	0.0078	1.8249	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	2	228	0.6367	0.0156	1.8811	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	3	342	0.6582	0.0312	2.0625	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0211	4	456	0.6446	0.0625	2.2743	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0211	5	570	0.6513	0.125	2.7714	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0211	6	684	0.6414	0.25	3.7161	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