gokuls / BERT-tiny-Massive-intent

huggingface.co
Total runs: 16
24-hour runs: 0
7-day runs: 4
30-day runs: 7
Model's Last Updated: September 24 2022
text-classification

Introduction of BERT-tiny-Massive-intent

Model Details of BERT-tiny-Massive-intent

BERT-tiny-Massive-intent

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the massive dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6740
  • Accuracy: 0.8475
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: 50
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Accuracy
3.6104 1.0 720 3.0911 0.3601
2.8025 2.0 1440 2.3800 0.5165
2.2292 3.0 2160 1.9134 0.5991
1.818 4.0 2880 1.5810 0.6744
1.5171 5.0 3600 1.3522 0.7108
1.2876 6.0 4320 1.1686 0.7442
1.1049 7.0 5040 1.0355 0.7683
0.9623 8.0 5760 0.9466 0.7885
0.8424 9.0 6480 0.8718 0.7875
0.7473 10.0 7200 0.8107 0.8028
0.6735 11.0 7920 0.7710 0.8180
0.6085 12.0 8640 0.7404 0.8210
0.5536 13.0 9360 0.7180 0.8229
0.5026 14.0 10080 0.6980 0.8318
0.4652 15.0 10800 0.6970 0.8337
0.4234 16.0 11520 0.6822 0.8372
0.3987 17.0 12240 0.6691 0.8436
0.3707 18.0 12960 0.6679 0.8455
0.3433 19.0 13680 0.6740 0.8475
0.3206 20.0 14400 0.6760 0.8451
0.308 21.0 15120 0.6704 0.8436
0.2813 22.0 15840 0.6701 0.8416
Framework versions
  • Transformers 4.22.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.5.1
  • Tokenizers 0.12.1

Runs of gokuls BERT-tiny-Massive-intent on huggingface.co

16
Total runs
0
24-hour runs
4
3-day runs
4
7-day runs
7
30-day runs

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BERT-tiny-Massive-intent install

BERT-tiny-Massive-intent is an open source model from GitHub that offers a free installation service, and any user can find BERT-tiny-Massive-intent on GitHub to install. At the same time, huggingface.co provides the effect of BERT-tiny-Massive-intent install, users can directly use BERT-tiny-Massive-intent installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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