jayavibhav / bert-classification-1500samples

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Total runs: 7
24-hour runs: 0
7-day runs: -2
30-day runs: 3
Model's Last Updated: August 08 2023
text-classification

Introduction of bert-classification-1500samples

Model Details of bert-classification-1500samples

bert-classification-1500samples

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4366
  • Accuracy: 0.882
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: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2
Training results
Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 47 0.2065 0.932
No log 2.0 94 0.4366 0.882
Framework versions
  • Transformers 4.30.2
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.13.3

Runs of jayavibhav bert-classification-1500samples on huggingface.co

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

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bert-classification-1500samples is an open source model from GitHub that offers a free installation service, and any user can find bert-classification-1500samples on GitHub to install. At the same time, huggingface.co provides the effect of bert-classification-1500samples install, users can directly use bert-classification-1500samples installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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jayavibhav
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