Instructions to use tanoManzo/nucleotide-transformer-2.5b-multi-species_ft_BioS45_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-2.5b-multi-species_ft_BioS45_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-2.5b-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanoManzo/nucleotide-transformer-2.5b-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot") model = AutoModelForSequenceClassification.from_pretrained("tanoManzo/nucleotide-transformer-2.5b-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd72f42a722894556401ab8ba8a5135806ff6ad0cf795665e94d1fb2a0cc349d
- Size of remote file:
- 5.37 kB
- SHA256:
- c9012621811a561ebc61978a1542c9974d8b46d8ea686cd0e32cbfbe8d588c42
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