Instructions to use tanoManzo/Geneformer_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/Geneformer_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/Geneformer_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanoManzo/Geneformer_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot") model = AutoModelForSequenceClassification.from_pretrained("tanoManzo/Geneformer_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 476652828fa9ed4078b43d3d7d7dabd15905924a5cc870792a4d05e5d159e412
- Size of remote file:
- 152 MB
- SHA256:
- 02caa432b62e84301339c7f83a927663836a85fe26fd91f0bc81eb2c677377f7
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