Noorrabie / bert-base-uncased-finetuned-multiple

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Model's Last Updated: November 09 2023
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Introduction of bert-base-uncased-finetuned-multiple

Model Details of bert-base-uncased-finetuned-multiple

bert-base-uncased-finetuned-multiple

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: 1.6040
  • Accuracy: 0.4250
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: 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 Accuracy
No log 1.0 10 1.6056 0.3500
No log 2.0 20 1.6051 0.4000
No log 3.0 30 1.6040 0.4250
Framework versions
  • Transformers 4.33.0
  • Pytorch 2.0.0+cpu
  • Datasets 2.1.0
  • Tokenizers 0.13.3

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

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