microsoft/scFM-dataselection开源了新模型

Microsoft microsoft/scFM-dataselection microsoft/scFM-dataselection https://huggingface.co/microsoft/scFM-dataselection

microsoft/scFM-dataselection · Hugging Face Hugging Face (https://huggingface.co/) Models (https://huggingface.co/models) Datasets (https://huggingface.co/datasets) Spaces (https://huggingface.co/spaces) Buckets new (https://huggingface.co/storage) Docs (https://huggingface.co/docs) Enterprise (https://huggingface.co/enterprise) Pricing (https://huggingface.co/pricing) Website Tasks (https://huggingface.co/tasks) HuggingChat (https://huggingface.co/chat) Collections (https://huggingface.co/collections) Languages (https://huggingface.co/languages) Organizations (https://huggingface.co/organizations) Community Blog (https://huggingface.co/blog) Posts (https://huggingface.co/posts) Daily Papers (https://huggingface.co/papers) Hardware (https://huggingface.co/hardware) Learn (https://huggingface.co/learn) Discord (https://huggingface.co/join/discord) Forum (https://discuss.huggingface.co/) GitHub (https://github.com/huggingface) Solutions Team & Enterprise (https://huggingface.co/enterprise) Hugging Face PRO (https://huggingface.co/pro) Enterprise Support (https://huggingface.co/support) Inference Providers (https://huggingface.co/inference/models) Inference Endpoints (https://huggingface.co/inference-endpoints) Storage Buckets (https://huggingface.co/storage) Log In (https://huggingface.co/login) Sign Up (https://huggingface.co/join) (https://huggingface.co/microsoft) microsoft (https://huggingface.co/microsoft) / scFM-dataselection (https://huggingface.co/microsoft/scFM-dataselection) Like 1 Follow Microsoft 22.6k Safetensors (https://huggingface.co/models?library=safetensors) single-cell (https://huggingface.co/models?other=single-cell) biology (https://huggingface.co/models?other=biology) geneformer (https://huggingface.co/models?other=geneformer) scvi (https://huggingface.co/models?other=scvi) self-supervised-learning (https://huggingface.co/models?other=self-supervised-learning) pca (https://huggingface.co/models?other=pca) License: mit Model card (https://huggingface.co/microsoft/scFM-dataselection) Files Files and versions xet (https://huggingface.co/microsoft/scFM-dataselection/tree/main) Community (https://huggingface.co/microsoft/scFM-dataselection/discussions) Copy to bucket new scFM-dataselection pretraining checkpoints (https://huggingface.co/microsoft/scFM-dataselection#scfm-dataselection-pretraining-checkpoints) Models (https://huggingface.co/microsoft/scFM-dataselection#models) Download (https://huggingface.co/microsoft/scFM-dataselection#download) Load a checkpoint (https://huggingface.co/microsoft/scFM-dataselection#load-a-checkpoint) License (https://huggingface.co/microsoft/scFM-dataselection#license) Citation (https://huggingface.co/microsoft/scFM-dataselection#citation) (https://huggingface.co/microsoft/scFM-dataselection#scfm-dataselection-pretraining-checkpoints) scFM-dataselection pretraining checkpoints Model checkpoints and PCA components for Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance, DenAdel et al., Nature Methods (2026). Paper (https://doi.org/10.1038/s41592-026-03120-y) Code and preprocessing (https://github.com/microsoft/scFM-dataselection) (https://huggingface.co/microsoft/scFM-dataselection#models) Models Model Count Geneformer 125 scVI 125 SSL/MLP 125 PCA components 80 Total 455 The full download is approximately 22.4 GB: 375 neural network checkpoints and 80 PCA component matrices. Within portable-checkpoints/, models are grouped into geneformer/, scvi/, ssl/ and pca/. The Geneformer, scVI and SSL models cover random sampling, cell-type reweighting and geometric sketching at 1, 10, 25, 50, 75 and 100 percent of the pretraining corpus. The collection also includes perturbation spike-in experiments. See model-index.csv for model folders and training settings. (https://huggingface.co/microsoft/scFM-dataselection#download) Download Use Python 3.10. The examples below were tested o