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DNADebertaSentencepiece30k
Vlasta/DNADebertaSentencepiece30k
DNADebertaSentencepiece30k is a fill-mask model from Vlasta. Use it when you need the model to fill a missing word. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Fill-Mask
- Library
- transformers
- Model type
- deberta
- Access
- Public
- Created
- Jul 28, 2022
- Updated
- Jul 31, 2022
- SHA
- 686ceb2b
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
DNADebertaSentencepiece30k
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.3257
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 7.9373 | 0.41 | 5000 | 7.8263 |
| 7.8005 | 0.81 | 10000 | 7.7871 |
| 7.7704 | 1.22 | 15000 | 7.7630 |
| 7.7477 | 1.62 | 20000 | 7.6857 |
| 7.6058 | 2.03 | 25000 | 7.5543 |
| 7.5281 | 2.44 | 30000 | 7.4839 |
| 7.4487 | 2.84 | 35000 | 7.3801 |
| 7.3368 | 3.25 | 40000 | 7.2603 |
| 7.1923 | 3.66 | 45000 | 7.0365 |
| 6.9858 | 4.06 | 50000 | 6.8793 |
| 6.8639 | 4.47 | 55000 | 6.7839 |
| 6.7877 | 4.87 | 60000 | 6.7176 |
| 6.728 | 5.28 | 65000 | 6.6680 |
| 6.6826 | 5.69 | 70000 | 6.6258 |
| 6.6414 | 6.09 | 75000 | 6.5847 |
| 6.6057 | 6.5 | 80000 | 6.5571 |
| 6.5794 | 6.91 | 85000 | 6.5279 |
| 6.5525 | 7.31 | 90000 | 6.5059 |
| 6.5354 | 7.72 | 95000 | 6.4816 |
| 6.5125 | 8.12 | 100000 | 6.4674 |
| 6.4958 | 8.53 | 105000 | 6.4486 |
| 6.4817 | 8.94 | 110000 | 6.4317 |
| 6.4674 | 9.34 | 115000 | 6.4195 |
| 6.4549 | 9.75 | 120000 | 6.4072 |
| 6.4409 | 10.16 | 125000 | 6.3945 |
| 6.4302 | 10.56 | 130000 | 6.3861 |
| 6.4214 | 10.97 | 135000 | 6.3755 |
| 6.4118 | 11.37 | 140000 | 6.3659 |
| 6.4058 | 11.78 | 145000 | 6.3604 |
| 6.3985 | 12.19 | 150000 | 6.3560 |
| 6.3899 | 12.59 | 155000 | 6.3473 |
| 6.3837 | 13.0 | 160000 | 6.3417 |
| 6.3782 | 13.41 | 165000 | 6.3361 |
| 6.3753 | 13.81 | 170000 | 6.3309 |
| 6.3733 | 14.22 | 175000 | 6.3285 |
| 6.3706 | 14.62 | 180000 | 6.3277 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
- Task
- Fill-Mask
- Library
- transformers
- Type
- deberta
- Created
- Jul 28, 2022
- Updated
- Jul 31, 2022