mabrouk / bert-base-uncased-issues-128

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Total runs: 12
24-hour runs: 0
7-day runs: -4
30-day runs: 3
Model's Last Updated: August 09 2023
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Introduction of bert-base-uncased-issues-128

Model Details of bert-base-uncased-issues-128

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: 1.4981
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: 3
Training results
Training Loss Epoch Step Validation Loss
2.125 1.0 291 1.7053
1.662 2.0 582 1.3900
1.5339 3.0 873 1.4981
Framework versions
  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3

Runs of mabrouk bert-base-uncased-issues-128 on huggingface.co

12
Total runs
0
24-hour runs
-2
3-day runs
-4
7-day runs
3
30-day runs

More Information About bert-base-uncased-issues-128 huggingface.co Model

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bert-base-uncased-issues-128 install

bert-base-uncased-issues-128 is an open source model from GitHub that offers a free installation service, and any user can find bert-base-uncased-issues-128 on GitHub to install. At the same time, huggingface.co provides the effect of bert-base-uncased-issues-128 install, users can directly use bert-base-uncased-issues-128 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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