jayavibhav / distilbert-classification-10ksamples

huggingface.co
Total runs: 5
24-hour runs: 0
7-day runs: -1
30-day runs: -6
Model's Last Updated: August 09 2023
text-classification

Introduction of distilbert-classification-10ksamples

Model Details of distilbert-classification-10ksamples

distilbert-classification-10ksamples

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

  • Loss: 0.1977
  • Accuracy: 0.96
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: 16
  • 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
0.1853 1.0 625 0.1682 0.9577
0.0436 2.0 1250 0.1977 0.96
Framework versions
  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3

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

5
Total runs
0
24-hour runs
-1
3-day runs
-1
7-day runs
-6
30-day runs

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distilbert-classification-10ksamples install

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