lenguist / my_awesome_model

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: 1
30-day runs: 3
Model's Last Updated: December 14 2023
text-classification

Introduction of my_awesome_model

Model Details of my_awesome_model

my_awesome_model

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

  • Loss: 0.2362
  • Accuracy: 0.9313
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
0.225 1.0 1563 0.2032 0.9207
0.1454 2.0 3126 0.2362 0.9313
Framework versions
  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0

Runs of lenguist my_awesome_model on huggingface.co

4
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3-day runs
1
7-day runs
3
30-day runs

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More my_awesome_model license Visit here:

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my_awesome_model huggingface.co

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lenguist my_awesome_model online free url in huggingface.co:

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my_awesome_model install

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

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