Instructions to use tanoManzo/nucleotide-transformer-2.5b-multi-species_ft_Hepg2_1kbpHG19_DHSs_H3K27AC 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_Hepg2_1kbpHG19_DHSs_H3K27AC 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_Hepg2_1kbpHG19_DHSs_H3K27AC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanoManzo/nucleotide-transformer-2.5b-multi-species_ft_Hepg2_1kbpHG19_DHSs_H3K27AC") model = AutoModelForSequenceClassification.from_pretrained("tanoManzo/nucleotide-transformer-2.5b-multi-species_ft_Hepg2_1kbpHG19_DHSs_H3K27AC") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4a663baa4fb6fa50644e824822a0a1fa37d5dbb54b6f86356483adfa9f0943d
- Size of remote file:
- 5.18 kB
- SHA256:
- 3dd8c933ef3d4c7d3473420fe4d003016033908908d31e0fa1852ba02d619ed5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.