testing1

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

• Loss: 0.7285
• Accuracy: 0.8928

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: 16
• eval_batch_size: 16
• seed: 33
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.4751	1.0	720	0.6154	0.8593
0.4363	2.0	1440	0.4945	0.8800
0.2325	3.0	2160	0.5608	0.8834
0.1349	4.0	2880	0.6023	0.8800
0.0887	5.0	3600	0.6301	0.8869
0.0575	6.0	4320	0.6649	0.8898
0.0358	7.0	5040	0.7023	0.8908
0.0218	8.0	5760	0.7419	0.8913
0.0184	9.0	6480	0.7285	0.8928
0.0126	10.0	7200	0.7207	0.8908
0.0072	11.0	7920	0.7453	0.8923
0.0056	12.0	8640	0.7746	0.8918

Framework versions

• Transformers 4.36.2
• Pytorch 2.0.1+cu117
• Datasets 2.15.0
• Tokenizers 0.15.0