Instructions to use tdc/Geneformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tdc/Geneformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tdc/Geneformer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tdc/Geneformer") model = AutoModelForMaskedLM.from_pretrained("tdc/Geneformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 737 Bytes
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# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.2.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-added-large-files
- id: check-merge-conflict
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- id: check-docstring-first
- repo: https://github.com/pycqa/isort
rev: 5.12.0
hooks:
- id: isort
args: ["--profile", "black"]
- repo: https://github.com/astral-sh/ruff-pre-commit
# Ruff version.
rev: v0.1.4
hooks:
# Run the Ruff linter.
- id: ruff
# Run the Ruff formatter.
- id: ruff-format
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