jayavibhav / deberta-classification-10ksamples

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Total runs: 5
24-hour runs: 0
7-day runs: -5
30-day runs: -3
Model's Last Updated: August 09 2023
text-classification

Introduction of deberta-classification-10ksamples

Model Details of deberta-classification-10ksamples

deberta-classification-10ksamples

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

  • Loss: 0.0480
  • Accuracy: 0.9906
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: 2e-05
  • train_batch_size: 16
  • 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
0.1035 1.0 625 0.0188 0.9951
0.0188 2.0 1250 0.0480 0.9906
Framework versions
  • Transformers 4.30.2
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.13.3

Runs of jayavibhav deberta-classification-10ksamples on huggingface.co

5
Total runs
0
24-hour runs
0
3-day runs
-5
7-day runs
-3
30-day runs

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deberta-classification-10ksamples install

deberta-classification-10ksamples is an open source model from GitHub that offers a free installation service, and any user can find deberta-classification-10ksamples on GitHub to install. At the same time, huggingface.co provides the effect of deberta-classification-10ksamples install, users can directly use deberta-classification-10ksamples 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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