2eaa88c95b4c68171cee280391c9f77c

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: 0.6587
• Data Size: 1.0
• Epoch Runtime: 651.8097
• Accuracy: 0.6320
• F1 Macro: 0.3872
• Rouge1: 0.6318
• Rouge2: 0.0
• Rougel: 0.6319
• Rougelsum: 0.6317

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	0.6906	0	21.6260	0.5498	0.4134	0.5498	0.0	0.5498	0.5497
0.5979	1	11370	0.6268	0.0078	26.9376	0.7366	0.6784	0.7366	0.0	0.7366	0.7366
0.4801	2	22740	0.4333	0.0156	31.9519	0.7970	0.7792	0.7970	0.0	0.7969	0.7969
0.4405	3	34110	0.4454	0.0312	41.6447	0.7847	0.7477	0.7847	0.0	0.7846	0.7847
0.3872	4	45480	0.4253	0.0625	61.1748	0.8258	0.8127	0.8258	0.0	0.8257	0.8258
0.676	5	56850	0.6675	0.125	101.8423	0.6320	0.3872	0.6318	0.0	0.6319	0.6317
0.6603	6	68220	0.6594	0.25	179.6352	0.6320	0.3872	0.6318	0.0	0.6319	0.6317
0.6586	7	79590	0.6582	0.5	339.8374	0.6320	0.3872	0.6318	0.0	0.6319	0.6317
0.6564	8.0	90960	0.6587	1.0	651.8097	0.6320	0.3872	0.6318	0.0	0.6319	0.6317

Framework versions

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