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ZINC-deberta

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deberta
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ZINC-deberta / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - sagawa/ZINC-canonicalized
metrics:
  - accuracy
model-index:
  - name: ZINC-deberta-base-output
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: sagawa/ZINC-canonicalized
          type: sagawa/ZINC-canonicalized
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9900059572833486

ZINC-deberta-base-output

This model is a fine-tuned version of microsoft/deberta-base on the sagawa/ZINC-canonicalized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0237
  • Accuracy: 0.9900

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: 5e-05
  • train_batch_size: 20
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Accuracy Validation Loss
0.045 1.06 100000 0.9842 0.0409
0.0372 2.13 200000 0.9864 0.0346
0.0337 3.19 300000 0.9874 0.0314
0.0318 4.25 400000 0.9882 0.0293
0.0296 5.31 500000 0.0277 0.9887
0.0289 6.38 600000 0.0264 0.9891
0.0267 7.44 700000 0.0253 0.9894
0.0261 8.5 800000 0.0243 0.9898
0.025 9.57 900000 0.0238 0.9900

Framework versions

  • Transformers 4.22.0.dev0
  • Pytorch 1.12.0
  • Datasets 2.4.1.dev0
  • Tokenizers 0.11.6