b591351626cedea1253e631ee07d078e

This model is a fine-tuned version of google-bert/bert-large-uncased-whole-word-masking-finetuned-squad on the fancyzhx/dbpedia_14 dataset. It achieves the following results on the evaluation set:

• Loss: 2.6451
• Data Size: 0.5
• Epoch Runtime: 860.8740
• Accuracy: 0.0714
• F1 Macro: 0.0095
• Rouge1: 0.0714
• Rouge2: 0.0
• Rougel: 0.0714
• Rougelsum: 0.0714

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	2.7485	0	54.4267	0.0714	0.0095	0.0715	0.0	0.0715	0.0714
0.1048	1	17500	0.0897	0.0078	68.3180	0.9832	0.9832	0.9832	0.0	0.9832	0.9832
0.0638	2	35000	0.1011	0.0156	78.6275	0.9823	0.9823	0.9823	0.0	0.9823	0.9823
0.0966	3	52500	0.0793	0.0312	103.7515	0.9860	0.9860	0.9860	0.0	0.9860	0.9860
0.1078	4	70000	0.0958	0.0625	151.6196	0.9834	0.9835	0.9835	0.0	0.9834	0.9834
0.0938	5	87500	0.1386	0.125	249.9378	0.9767	0.9766	0.9767	0.0	0.9767	0.9767
2.6685	6	105000	2.6482	0.25	449.2338	0.0714	0.0095	0.0714	0.0	0.0714	0.0714
0.0151	7	122500	2.6451	0.5	860.8740	0.0714	0.0095	0.0714	0.0	0.0714	0.0714

Framework versions

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