# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_Jan-6_tokenized")
model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_Jan-6_tokenized")Quick Links
BERT_Jan-6_tokenized
This model is a fine-tuned version of armheb/DNA_bert_6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0369
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: 64
- eval_batch_size: 64
- 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 |
|---|---|---|---|
| 0.0695 | 1.0 | 187 | 0.0404 |
| 0.0398 | 2.0 | 374 | 0.0388 |
| 0.0386 | 3.0 | 561 | 0.0362 |
| 0.0385 | 4.0 | 748 | 0.0378 |
| 0.0376 | 5.0 | 935 | 0.0358 |
| 0.0377 | 6.0 | 1122 | 0.0357 |
| 0.0378 | 7.0 | 1309 | 0.0377 |
| 0.0369 | 8.0 | 1496 | 0.0383 |
| 0.0374 | 9.0 | 1683 | 0.0364 |
| 0.0359 | 10.0 | 1870 | 0.0335 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
- Downloads last month
- 22
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_Jan-6_tokenized")