Instructions to use tanoManzo/nucleotide-transformer-v2-250m-multi-species_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanoManzo/nucleotide-transformer-v2-250m-multi-species_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tanoManzo/nucleotide-transformer-v2-250m-multi-species_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanoManzo/nucleotide-transformer-v2-250m-multi-species_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("tanoManzo/nucleotide-transformer-v2-250m-multi-species_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f16616641c88301607274420cc06b1be9e2bd9ed316d0ebac52017ab8aa411a
- Size of remote file:
- 928 MB
- SHA256:
- 3cd7b243f8d8b05667bdafb0bd82402a75c6d5a83dd5ad5049bf8d5aae9b39b3
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