gokuls / add_BERT_48_qnli

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Total runs: 21
24-hour runs: 0
7-day runs: 4
30-day runs: 8
Model's Last Updated: June 16 2023
text-classification

Introduction of add_BERT_48_qnli

Model Details of add_BERT_48_qnli

add_BERT_48_qnli

This model is a fine-tuned version of gokuls/add_bert_12_layer_model_complete_training_new_48 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6899
  • Accuracy: 0.5263
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: 4e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 10
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
Training results
Training Loss Epoch Step Validation Loss Accuracy
0.6959 1.0 819 0.6933 0.5103
0.6945 2.0 1638 0.6917 0.4946
0.6938 3.0 2457 0.6899 0.5263
0.6925 4.0 3276 0.6903 0.5318
0.6961 5.0 4095 0.6940 0.5222
0.6929 6.0 4914 0.6914 0.5252
0.6922 7.0 5733 0.6921 0.5043
0.692 8.0 6552 0.6904 0.5303
Framework versions
  • Transformers 4.30.2
  • Pytorch 1.14.0a0+410ce96
  • Datasets 2.13.0
  • Tokenizers 0.13.3

Runs of gokuls add_BERT_48_qnli on huggingface.co

21
Total runs
0
24-hour runs
3
3-day runs
4
7-day runs
8
30-day runs

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add_BERT_48_qnli install

add_BERT_48_qnli is an open source model from GitHub that offers a free installation service, and any user can find add_BERT_48_qnli on GitHub to install. At the same time, huggingface.co provides the effect of add_BERT_48_qnli install, users can directly use add_BERT_48_qnli installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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https://huggingface.co/gokuls/add_BERT_48_qnli

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