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

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taoyoung/
bert-base-uncased-issues-128

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google-bert/bert-base-uncased

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bert-base-uncased-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: 1.2249

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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 16

Training results

Training Loss	Epoch	Step	Validation Loss
2.0964	1.0	291	1.6920
1.6326	2.0	582	1.5200
1.4972	3.0	873	1.3468
1.3962	4.0	1164	1.3353
1.3326	5.0	1455	1.2486
1.2832	6.0	1746	1.3706
1.2324	7.0	2037	1.2960
1.2027	8.0	2328	1.3538
1.1663	9.0	2619	1.2239
1.1401	10.0	2910	1.1766
1.1265	11.0	3201	1.1296
1.1111	12.0	3492	1.1792
1.0887	13.0	3783	1.2181
1.0759	14.0	4074	1.2107
1.0713	15.0	4365	1.2353
1.0639	16.0	4656	1.2249

Framework versions

Transformers 4.38.2
Pytorch 2.2.2+cu118
Datasets 2.19.0
Tokenizers 0.15.2