67acc79d6e137c2bb16eda40bfc3ffff

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

• Loss: 0.7127
• Data Size: 1.0
• Epoch Runtime: 1.5396
• 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	0.6866	0	0.6232	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	1	19	0.7129	0.0078	1.2328	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
No log	2	38	0.7485	0.0156	0.7367	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
No log	3	57	0.7274	0.0312	0.7944	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
No log	4	76	0.6907	0.0625	0.8175	0.5469	0.3535	0.5469	0.0	0.5469	0.5469
No log	5	95	0.6918	0.125	0.8528	0.5469	0.3535	0.5469	0.0	0.5469	0.5469
0.08	6	114	0.6932	0.25	0.9792	0.5625	0.3905	0.5625	0.0	0.5625	0.5625
0.08	7	133	0.7087	0.5	1.1356	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
0.522	8.0	152	0.7127	1.0	1.5396	0.4375	0.3043	0.4375	0.0	0.4375	0.4375

Framework versions

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