mateiaass / albert-base-qa-coQA-2-k-fold-2

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Total runs: 14
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30-day runs: 9
Model's Last Updated: October 28 2023
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Introduction of albert-base-qa-coQA-2-k-fold-2

Model Details of albert-base-qa-coQA-2-k-fold-2

albert-base-qa-coQA-2-k-fold-2

This model is a fine-tuned version of albert-base-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.6875
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: 3
Training results
Training Loss Epoch Step Validation Loss
2.6739 1.0 5467 2.6393
2.3608 2.0 10934 2.5973
2.0412 3.0 16401 2.6875
Framework versions
  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1

Runs of mateiaass albert-base-qa-coQA-2-k-fold-2 on huggingface.co

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albert-base-qa-coQA-2-k-fold-2 install

albert-base-qa-coQA-2-k-fold-2 is an open source model from GitHub that offers a free installation service, and any user can find albert-base-qa-coQA-2-k-fold-2 on GitHub to install. At the same time, huggingface.co provides the effect of albert-base-qa-coQA-2-k-fold-2 install, users can directly use albert-base-qa-coQA-2-k-fold-2 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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