Instructions to use roa7n/DNABert_K6_G_quad_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roa7n/DNABert_K6_G_quad_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="roa7n/DNABert_K6_G_quad_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("roa7n/DNABert_K6_G_quad_1") model = AutoModelForSequenceClassification.from_pretrained("roa7n/DNABert_K6_G_quad_1") - Notebooks
- Google Colab
- Kaggle
DNABert_K6_G_quad_1
This model is a fine-tuned version of armheb/DNA_bert_6 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0803
- Accuracy: 0.9720
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0926 | 1.0 | 9375 | 0.0803 | 0.9720 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
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