groar / distilgpt2-finetuned-wikitext2

huggingface.co
Total runs: 30
24-hour runs: 0
7-day runs: 1
30-day runs: 5
Model's Last Updated: February 05 2022
text-generation

Introduction of distilgpt2-finetuned-wikitext2

Model Details of distilgpt2-finetuned-wikitext2

distilgpt2-finetuned-wikitext2

This model is a fine-tuned version of distilgpt2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 3.6895
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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1
Training results
Training Loss Epoch Step Validation Loss
3.7852 1.0 2334 3.6895
Framework versions
  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0

Runs of groar distilgpt2-finetuned-wikitext2 on huggingface.co

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

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distilgpt2-finetuned-wikitext2 install

distilgpt2-finetuned-wikitext2 is an open source model from GitHub that offers a free installation service, and any user can find distilgpt2-finetuned-wikitext2 on GitHub to install. At the same time, huggingface.co provides the effect of distilgpt2-finetuned-wikitext2 install, users can directly use distilgpt2-finetuned-wikitext2 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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Updated:August 23 2022