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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26828e0a29209f482125a30b43e146224ee4494a649df148d3e35a583924e713
- Size of remote file:
- 468 MB
- SHA256:
- 2b2eb5d3832ef2311a3b4ef7f650df5ea246a24065010258250d9080658a78ce
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