Instructions to use ctheodoris/Geneformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctheodoris/Geneformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ctheodoris/Geneformer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ctheodoris/Geneformer") model = AutoModelForMaskedLM.from_pretrained("ctheodoris/Geneformer", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Adding tags to the model
Browse filesHi, your HuggingFace page is awesome. Adding both tags will help users to find these models and hopefully we get more models available on Huggingface in the single-cell field.
Can (Lead developer of scvi-tools)
README.md
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datasets: ctheodoris/Genecorpus-30M
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license: apache-2.0
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# Geneformer
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Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
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datasets: ctheodoris/Genecorpus-30M
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license: apache-2.0
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tags:
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- single-cell
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- genomics
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# Geneformer
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Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
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