Instructions to use tanoManzo/nucleotide-transformer-500m-human-ref_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-500m-human-ref_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-500m-human-ref_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot") model = AutoModelForSequenceClassification.from_pretrained("tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 84a4d52a5a149f99a418bf0a553264dd0770547da322c83752169298886b3cee
- Size of remote file:
- 5.37 kB
- SHA256:
- d5d2f8a3235dcf92eb2ecc5122a4035bd39e2f73007d59a118d019e50d5d23f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.