f22dfa2587c66d1a37813ed686eb4160

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

• Loss: 0.6211
• Data Size: 1.0
• Epoch Runtime: 16.2804
• Accuracy: 0.6885
• F1 Macro: 0.4078
• Rouge1: 0.6895
• Rouge2: 0.0
• Rougel: 0.6885
• Rougelsum: 0.6885

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	0.7171	0	1.1556	0.4160	0.4150	0.4160	0.0	0.4155	0.4160
No log	1	267	0.6419	0.0078	2.5499	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	2	534	0.6378	0.0156	1.4779	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	3	801	0.6062	0.0312	1.8003	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	4	1068	0.5717	0.0625	2.1973	0.6875	0.4511	0.6875	0.0	0.6875	0.6865
0.0356	5	1335	0.7067	0.125	3.1931	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.5799	6	1602	0.6336	0.25	5.0357	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6215	7	1869	0.6427	0.5	8.6479	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6149	8.0	2136	0.6211	1.0	16.2804	0.6885	0.4078	0.6895	0.0	0.6885	0.6885

Framework versions

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