Instructions to use jinbo1129/try2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinbo1129/try2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jinbo1129/try2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jinbo1129/try2") model = AutoModelForMaskedLM.from_pretrained("jinbo1129/try2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from setuptools import setup | |
| setup( | |
| name="geneformer", | |
| version="0.0.1", | |
| author="Christina Theodoris", | |
| author_email="christina.theodoris@gladstone.ucsf.edu", | |
| description="Geneformer is a transformer model pretrained \ | |
| on a large-scale corpus of ~30 million single \ | |
| cell transcriptomes to enable context-aware \ | |
| predictions in settings with limited data in \ | |
| network biology.", | |
| packages=["geneformer"], | |
| include_package_data=True, | |
| install_requires=[ | |
| "datasets", | |
| "loompy", | |
| "numpy", | |
| "transformers", | |
| ], | |
| ) | |