bert-base-uncased-issues-128-issues-128 – AI Model by RyotaBannai | AlphaNeural AI

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RyotaBannai/
bert-base-uncased-issues-128-issues-128

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google-bert/bert-base-uncased

autotrain_compatible

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bert-base-uncased-issues-128-issues-128

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2450

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: 128
eval_batch_size: 32
seed: 42
optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 16

Training results

Training Loss	Epoch	Step	Validation Loss
2.34	1.0	73	1.7904
1.7984	2.0	146	1.6072
1.6309	3.0	219	1.5297
1.531	4.0	292	1.3623
1.4659	5.0	365	1.3820
1.4121	6.0	438	1.3859
1.3798	7.0	511	1.3958
1.3421	8.0	584	1.2959
1.316	9.0	657	1.2213
1.2888	10.0	730	1.2518
1.278	11.0	803	1.2468
1.2624	12.0	876	1.2344
1.2522	13.0	949	1.3239
1.238	14.0	1022	1.2608
1.228	15.0	1095	1.2408
1.2116	16.0	1168	1.2450

Framework versions

Transformers 4.55.4
Pytorch 2.8.0+cu128
Datasets 2.21.0
Tokenizers 0.21.0