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contemmcm/
9bf9ddc254c9b36090eda65071e5518e

generated_from_trainer

google-bert/bert-base-uncased

finetune

apache-2.0

text-embeddings-inference

endpoints_compatible

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9bf9ddc254c9b36090eda65071e5518e

This model is a fine-tuned version of google-bert/bert-base-uncased on the fancyzhx/dbpedia_14 dataset. It achieves the following results on the evaluation set:

Loss: 0.5435
Data Size: 1.0
Epoch Runtime: 847.3748
Accuracy: 0.8641
F1 Macro: 0.8444
Rouge1: 0.8642
Rouge2: 0.0
Rougel: 0.8642
Rougelsum: 0.8642

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	Rougel	Rougelsum
No log	0	0	2.6998	0	29.8279	0.0478	0.0297	0.0478	0.0479	0.0478
0.1754	1	17500	0.0732	0.0078	36.2888	0.9871	0.9871	0.9871	0.9871	0.9871
0.056	2	35000	0.0636	0.0156	41.8011	0.9870	0.9870	0.9870	0.9870	0.9870
0.0399	3	52500	0.0633	0.0312	54.3729	0.9874	0.9874	0.9874	0.9874	0.9874
0.05	4	70000	0.0632	0.0625	78.7061	0.9868	0.9868	0.9868	0.9868	0.9868
0.0583	5	87500	0.0642	0.125	135.1392	0.9877	0.9877	0.9878	0.9877	0.9878
0.0461	6	105000	0.0696	0.25	229.4951	0.9861	0.9861	0.9861	0.9861	0.9861
0.0004	7	122500	0.0495	0.5	428.1039	0.9887	0.9887	0.9887	0.9887	0.9887
0.0372	8.0	140000	0.0480	1.0	830.1501	0.9906	0.9906	0.9906	0.9906	0.9906
0.0247	9.0	157500	0.0694	1.0	829.1238	0.9892	0.9893	0.9893	0.9892	0.9893
0.0261	10.0	175000	0.0742	1.0	831.0987	0.9885	0.9885	0.9885	0.9885	0.9885
0.0441	11.0	192500	0.2382	1.0	835.1891	0.8949	0.8817	0.8950	0.8950	0.8949
0.0474	12.0	210000	0.5435	1.0	847.3748	0.8641	0.8444	0.8642	0.8642	0.8642

Framework versions

Transformers 4.57.0
Pytorch 2.8.0+cu128
Datasets 4.3.0
Tokenizers 0.22.1

contemmcm/9bf9ddc254c9b36090eda65071e5518e

9bf9ddc254c9b36090eda65071e5518e

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

contemmcm/
9bf9ddc254c9b36090eda65071e5518e