Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,90 @@
+---
+license: cc-by-4.0
+library_name: scvi-tools
+tags:
+- biology
+- genomics
+- single-cell
+- model_cls_name:SCANVI
+- scvi_version:1.0.0
+- anndata_version:0.9.1
+- modality:rna
+- annotated:True
+---
+
+# Description
+
+Human scANVI reference model
+
+# Model properties
+
+Many model properties are in the model tags. Some more are listed below.
+
+**model_init_params**:
+```json
+{
+    "n_hidden": 128,
+    "n_latent": 10,
+    "n_layers": 2,
+    "dropout_rate": 0.1,
+    "dispersion": "gene",
+    "gene_likelihood": "nb",
+    "linear_classifier": false,
+    "latent_distribution": "normal"
+}
+```
+
+**model_setup_anndata_args**:
+```json
+{
+    "labels_key": "ct_fine",
+    "unlabeled_category": "Unknown",
+    "layer": "counts",
+    "batch_key": "batch",
+    "size_factor_key": null,
+    "categorical_covariate_keys": null,
+    "continuous_covariate_keys": null
+}
+```
+
+**model_summary_stats**:
+|     Summary Stat Key     | Value |
+|--------------------------|-------|
+|         n_batch          |   6   |
+|         n_cells          | 2323  |
+| n_extra_categorical_covs |   0   |
+| n_extra_continuous_covs  |   0   |
+|         n_labels         |  15   |
+|          n_vars          | 3000  |
+
+**model_data_registry**:
+| Registry Key |    scvi-tools Location    |
+|--------------|---------------------------|
+|      X       |  adata.layers['counts']   |
+|    batch     | adata.obs['_scvi_batch']  |
+|    labels    | adata.obs['_scvi_labels'] |
+
+**model_parent_module**: https://zenodo.org/records/10669600/files/32_human_adata.h5ad?download=1
+
+**data_is_minified**: False
+
+# Training data
+
+This is an optional link to where the training data is stored if it is too large
+to host on the huggingface Model hub.
+
+<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
+sure to provide this field if you want users to be able to access your training data. See the scvi-tools
+documentation for details. -->
+
+Training data url: https://github.com/brickmanlab/proks-salehin-et-al
+
+# Training code
+
+This is an optional link to the code used to train the model.
+
+Training code url: N/A
+
+# References
+
+Proks, Salehin et al., biorXiv