2dc3f03d6cc4627fa2c4f64dcc291ed6

This model is a fine-tuned version of distilbert/distilbert-base-cased-distilled-squad on the nyu-mll/glue [cola] dataset. It achieves the following results on the evaluation set:

• Loss: 0.8327
• Data Size: 1.0
• Epoch Runtime: 8.0725
• Accuracy: 0.7773
• F1 Macro: 0.7252
• Rouge1: 0.7783
• Rouge2: 0.0
• Rougel: 0.7773
• Rougelsum: 0.7773

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	0.7422	0	0.8494	0.3115	0.2375	0.3105	0.0	0.3115	0.3115
No log	1	267	0.6176	0.0078	1.3862	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	2	534	0.6851	0.0156	1.3316	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	3	801	0.6546	0.0312	1.5753	0.6895	0.4111	0.6904	0.0	0.6895	0.6895
No log	4	1068	0.6140	0.0625	1.7824	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.0358	5	1335	0.6755	0.125	2.2524	0.6963	0.4369	0.6963	0.0	0.6963	0.6963
0.5317	6	1602	0.5562	0.25	3.1334	0.7266	0.6025	0.7266	0.0	0.7266	0.7266
0.446	7	1869	0.5933	0.5	4.7171	0.7266	0.5531	0.7275	0.0	0.7266	0.7266
0.411	8.0	2136	0.5702	1.0	8.2503	0.7520	0.6483	0.7520	0.0	0.7529	0.7520
0.2599	9.0	2403	0.6141	1.0	8.0183	0.7676	0.6917	0.7676	0.0	0.7676	0.7676
0.1909	10.0	2670	0.8327	1.0	8.0725	0.7773	0.7252	0.7783	0.0	0.7773	0.7773

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.1