Patcas / my_awesome-assert-new

huggingface.co
Total runs: 15
24-hour runs: 1
7-day runs: 3
30-day runs: 5
Model's Last Updated: January 28 2024
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Introduction of my_awesome-assert-new

Model Details of my_awesome-assert-new

my_awesome-assert-new

This model is a fine-tuned version of Patcas/plbart-works on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4168
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: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4
Training results
Training Loss Epoch Step Validation Loss
0.4144 1.0 15052 0.4181
0.3772 2.0 30104 0.3961
0.358 3.0 45156 0.4086
0.3221 4.0 60208 0.4168
Framework versions
  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1

Runs of Patcas my_awesome-assert-new on huggingface.co

15
Total runs
1
24-hour runs
3
3-day runs
3
7-day runs
5
30-day runs

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Patcas my_awesome-assert-new online free url in huggingface.co:

https://huggingface.co/Patcas/my_awesome-assert-new

my_awesome-assert-new install

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

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https://huggingface.co/Patcas/my_awesome-assert-new

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