11de6f8e803653f8816fcd27edebb221

This model is a fine-tuned version of google-bert/bert-large-uncased-whole-word-masking-finetuned-squad on the contemmcm/cls_20newsgroups dataset. It achieves the following results on the evaluation set:

• Loss: 3.0606
• Data Size: 1.0
• Epoch Runtime: 70.6902
• Accuracy: 0.0554
• F1 Macro: 0.0053

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
No log	0	0	3.1249	0	4.6390	0.0423	0.0041
No log	1	499	3.0526	0.0078	5.3256	0.0590	0.0114
0.0308	2	998	3.0353	0.0156	6.0687	0.0423	0.0041
0.0549	3	1497	3.0356	0.0312	7.7245	0.0423	0.0041
0.103	4	1996	3.0274	0.0625	10.6392	0.0491	0.0047
3.0244	5	2495	3.0212	0.125	14.2042	0.0544	0.0052
3.0273	6	2994	3.0079	0.25	22.2859	0.0532	0.0050
3.0256	7	3493	3.0139	0.5	38.6885	0.0532	0.0050
3.0203	8.0	3992	3.0027	1.0	71.7315	0.0496	0.0047
3.0199	9.0	4491	3.0133	1.0	70.6367	0.0491	0.0047
3.0089	10.0	4990	3.0495	1.0	70.9631	0.0554	0.0053
3.0082	11.0	5489	3.0875	1.0	70.2922	0.0554	0.0053
3.0001	12.0	5988	3.0606	1.0	70.6902	0.0554	0.0053

Framework versions

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