gokulsrinivasagan / bert-base-uncased_rte

huggingface.co
Total runs: 10
24-hour runs: 0
7-day runs: 2
30-day runs: 6
Model's Last Updated: December 05 2024
text-classification

Introduction of bert-base-uncased_rte

Model Details of bert-base-uncased_rte

bert-base-uncased_rte

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

  • Loss: 0.6911
  • Accuracy: 0.5271
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: 0.001
  • train_batch_size: 256
  • eval_batch_size: 256
  • seed: 10
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 50
Training results
Training Loss Epoch Step Validation Loss Accuracy
0.9089 1.0 10 0.7181 0.5271
0.7168 2.0 20 0.7477 0.4729
0.7124 3.0 30 0.6934 0.5271
0.7066 4.0 40 0.6911 0.5271
0.7038 5.0 50 0.6953 0.5271
0.7089 6.0 60 0.6939 0.4729
0.7111 7.0 70 0.6979 0.5271
0.7051 8.0 80 0.6918 0.5271
0.7079 9.0 90 0.7641 0.4729
Framework versions
  • Transformers 4.46.3
  • Pytorch 2.2.1+cu118
  • Datasets 2.17.0
  • Tokenizers 0.20.3

Runs of gokulsrinivasagan bert-base-uncased_rte on huggingface.co

10
Total runs
0
24-hour runs
0
3-day runs
2
7-day runs
6
30-day runs

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bert-base-uncased_rte install

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

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