Update README.md

README.md

@@ -16,13 +16,19 @@ Mapping gene networks requires large amounts of transcriptomic data to learn the
 from tdc.model_server.tokenizers.geneformer import GeneformerTokenizer
 from tdc import tdc_hf_interface
 import torch
-# Retrieve anndata object. Then,
+# Retrieve anndata object. Then, tokenize
 tokenizer = GeneformerTokenizer()
 x = tokenizer.tokenize_cell_vectors(adata,
                                     ensembl_id="feature_id",
                                     ncounts="n_measured_vars")
 cells, _ = x
 input_tensor = torch.tensor(cells) # note that you may need to pad or perform other custom data processing
+
+# retrieve model
+geneformer = tdc_hf_interface("Geneformer")
+model = geneformer.load()
+
+# run inference
 attention_mask = torch.tensor(
     [[x[0] != 0, x[1] != 0] for x in input_tensor]) # here we assume we used 0/False as a special padding token
 outputs = model(batch,