Instructions to use tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS74_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-500m-human-ref_ft_BioS74_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-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC_one_shot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC_one_shot") model = AutoModelForSequenceClassification.from_pretrained("tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC_one_shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6daf19cd8935712c482bd276d290716f55915bbd5b0d209eb3e96e4f0b8f5454
- Size of remote file:
- 1.92 GB
- SHA256:
- 994b0029583dd3ef9af65a953a58857da76c1324c6a56c83320a71caba1db8cb
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