# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/DNA_BigBird_3")
model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/DNA_BigBird_3")Quick Links
dna_3-Pretrained
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6003
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: 3e-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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6057 | 1.0 | 62 | 0.6032 |
| 0.5983 | 2.0 | 124 | 0.6002 |
| 0.5934 | 3.0 | 186 | 0.5992 |
| 0.5853 | 4.0 | 248 | 0.5979 |
| 0.5799 | 5.0 | 310 | 0.5982 |
| 0.5769 | 6.0 | 372 | 0.5999 |
| 0.5754 | 7.0 | 434 | 0.5996 |
| 0.5713 | 8.0 | 496 | 0.6000 |
| 0.5687 | 9.0 | 558 | 0.5988 |
| 0.5666 | 10.0 | 620 | 0.6003 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/DNA_BigBird_3")