62b0963eb64bda721100b62b25dd762a

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

• Loss: 0.5367
• Data Size: 1.0
• Epoch Runtime: 68.3747
• Accuracy: 0.8762
• F1 Macro: 0.8755
• Rouge1: 0.8762
• Rouge2: 0.0
• Rougel: 0.8762
• Rougelsum: 0.8762

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	0.6927	0	0.8990	0.5278	0.4376	0.5272	0.0	0.5278	0.5278
No log	1	2104	0.6886	0.0078	1.8391	0.5174	0.3690	0.5174	0.0	0.5174	0.5174
No log	2	4208	0.4883	0.0156	1.9152	0.7697	0.7697	0.7685	0.0	0.7697	0.7685
0.0119	3	6312	0.4591	0.0312	2.9868	0.7801	0.7796	0.7801	0.0	0.7812	0.7801
0.4103	4	8416	0.4056	0.0625	5.0783	0.8264	0.8263	0.8264	0.0	0.8264	0.8264
0.3529	5	10520	0.3479	0.125	9.3437	0.8600	0.8598	0.8600	0.0	0.8600	0.8611
0.2802	6	12624	0.3522	0.25	17.6629	0.8519	0.8518	0.8519	0.0	0.8530	0.8519
0.2454	7	14728	0.3226	0.5	34.4643	0.875	0.875	0.875	0.0	0.875	0.875
0.2098	8.0	16832	0.3422	1.0	68.1054	0.8773	0.8771	0.8773	0.0	0.8773	0.8773
0.1597	9.0	18936	0.3896	1.0	68.6002	0.8819	0.8818	0.8819	0.0	0.8819	0.8819
0.1514	10.0	21040	0.3676	1.0	68.2303	0.8843	0.8842	0.8843	0.0	0.8843	0.8843
0.1191	11.0	23144	0.5367	1.0	68.3747	0.8762	0.8755	0.8762	0.0	0.8762	0.8762

Framework versions

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