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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- sagawa/ZINC-canonicalized |
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metrics: |
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- accuracy |
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model-index: |
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- name: ZINC-deberta-base-output |
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results: |
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- task: |
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name: Masked Language Modeling |
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type: fill-mask |
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dataset: |
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name: sagawa/ZINC-canonicalized |
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type: sagawa/ZINC-canonicalized |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9900059572833486 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ZINC-deberta-base-output |
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co./microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0237 |
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- Accuracy: 0.9900 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 20 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:------:|:--------:|:---------------:| |
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| 0.045 | 1.06 | 100000 | 0.9842 | 0.0409 | |
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| 0.0372 | 2.13 | 200000 | 0.9864 | 0.0346 | |
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| 0.0337 | 3.19 | 300000 | 0.9874 | 0.0314 | |
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| 0.0318 | 4.25 | 400000 | 0.9882 | 0.0293 | |
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| 0.0296 | 5.31 | 500000 | 0.0277 | 0.9887 | |
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| 0.0289 | 6.38 | 600000 | 0.0264 | 0.9891 | |
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| 0.0267 | 7.44 | 700000 | 0.0253 | 0.9894 | |
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| 0.0261 | 8.5 | 800000 | 0.0243 | 0.9898 | |
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| 0.025 | 9.57 | 900000 | 0.0238 | 0.9900 | |
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### Framework versions |
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- Transformers 4.22.0.dev0 |
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- Pytorch 1.12.0 |
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- Datasets 2.4.1.dev0 |
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- Tokenizers 0.11.6 |
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