Instructions to use rashiqua/dnabert2_epigenetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rashiqua/dnabert2_epigenetic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rashiqua/dnabert2_epigenetic", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rashiqua/dnabert2_epigenetic", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("rashiqua/dnabert2_epigenetic", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51ba133e76cd1052e66c1d073edd18fc7b88052919456b761a0fae3b5426bb69
- Size of remote file:
- 937 MB
- SHA256:
- 8ff7f4efd3badac7b06aec2c0de46e83e0d603b4ae8bd8f0e7847591700b926a
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