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

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jmassot/
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: 1.2512

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
2.1019	1.0	291	1.7019
1.6412	2.0	582	1.4273
1.4844	3.0	873	1.3947
1.4006	4.0	1164	1.3698
1.3382	5.0	1455	1.1941
1.2822	6.0	1746	1.2781
1.2393	7.0	2037	1.2650
1.2009	8.0	2328	1.2082
1.1657	9.0	2619	1.1776
1.1394	10.0	2910	1.2050
1.1276	11.0	3201	1.2067
1.1051	12.0	3492	1.1630
1.0814	13.0	3783	1.2529
1.0757	14.0	4074	1.1699
1.063	15.0	4365	1.1113
1.0637	16.0	4656	1.2512

Framework versions

Transformers 4.11.3
Pytorch 1.11.0+cu113
Datasets 1.16.1
Tokenizers 0.10.1