9d0a7e98352f37f9a91444c570282617

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: 0.7327
• Data Size: 1.0
• Epoch Runtime: 1.8851
• Accuracy: 0.4375
• F1 Macro: 0.3043
• Rouge1: 0.4375
• Rouge2: 0.0
• Rougel: 0.4375
• Rougelsum: 0.4375

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	0.6977	0	0.6142	0.5156	0.5059	0.5	0.0	0.5156	0.5156
No log	1	19	0.8054	0.0078	1.0918	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
No log	2	38	0.7170	0.0156	0.7459	0.4375	0.3892	0.4375	0.0	0.4375	0.4375
No log	3	57	0.7362	0.0312	0.7390	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	4	76	0.6882	0.0625	0.8016	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	5	95	0.7316	0.125	0.8554	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
0.0818	6	114	0.7090	0.25	0.9606	0.5625	0.36	0.5625	0.0	0.5625	0.5625
0.0818	7	133	0.6989	0.5	1.3538	0.4844	0.4345	0.4844	0.0	0.4844	0.4844
0.5343	8.0	152	0.7327	1.0	1.8851	0.4375	0.3043	0.4375	0.0	0.4375	0.4375

Framework versions

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