35b96e33a85d72fe1d21de2af5344577

This model is a fine-tuned version of albert/albert-xlarge-v2 on the nyu-mll/glue dataset. It achieves the following results on the evaluation set:

• Loss: 0.7461
• Data Size: 0.125
• Epoch Runtime: 18.2523
• Accuracy: 0.5093
• F1 Macro: 0.3374
• Rouge1: 0.5093
• Rouge2: 0.0
• Rougel: 0.5093
• Rougelsum: 0.5081

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	0.7831	0	1.1613	0.4907	0.3292	0.4907	0.0	0.4907	0.4919
No log	1	2104	0.6978	0.0078	3.4121	0.5093	0.3374	0.5093	0.0	0.5093	0.5081
No log	2	4208	0.7473	0.0156	3.3892	0.5093	0.3374	0.5093	0.0	0.5093	0.5081
0.0158	3	6312	0.7109	0.0312	5.5223	0.5093	0.3374	0.5093	0.0	0.5093	0.5081
0.7103	4	8416	0.6980	0.0625	9.7736	0.4907	0.3292	0.4907	0.0	0.4907	0.4919
0.7087	5	10520	0.7461	0.125	18.2523	0.5093	0.3374	0.5093	0.0	0.5093	0.5081

Framework versions

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