Update README.md

README.md

@@ -18,10 +18,10 @@ This model is a VGCN-BERT model based on [DistilBert-base-uncased](https://huggi
 
 ```python
 import transformers as tfr
-from transformers.models.vgcn_bert.modeling_graph import WordGraph,_normalize_adj
+from transformers.models.vgcn_bert.modeling_graph import WordGraph
 
 tokenizer = tfr.AutoTokenizer.from_pretrained(
-    "zhibinlu/vgcn-bert-distilbert-base-uncased"
+    "zhibinlu/distilbert-base-uncased"
 )
 # 1st method: Build graph using NPMI statistical method from training corpus
 wgraph = WordGraph(rows=train_valid_df["text"], tokenizer=tokenizer)
@@ -42,7 +42,7 @@ from transformers.models.vgcn_bert.modeling_vgcn_bert import VGCNBertModel
 model = VGCNBertModel.from_pretrained(
     "zhibinlu/vgcn-bert-distilbert-base-uncased", trust_remote_code=True,
     wgraphs=[wgraph.to_torch_sparse()],
-    wgraph_id_to_tokenizer_id_maps=[wgraph.wgraph_id_to_tokenizer_id_map])
+    wgraph_id_to_tokenizer_id_maps=[wgraph.wgraph_id_to_tokenizer_id_map]
 )
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors="pt")