diff --git a/README.md b/README.md
index 9372f57..1d4662f 100644
--- a/README.md
+++ b/README.md
@@ -115,6 +115,36 @@ adj_mat = (i[:, None] >= (i[None, :] - 1)) & (i[:, None] <= (i[None, :] + 1))
 feats_out, coors_out = net(feats, coors, mask = mask, adj_mat = adj_mat) # (1, 1024, 32), (1, 1024, 3)
 ```
 
+## Edges
+
+If you need to pass in continuous edges
+
+```python
+import torch
+from egnn_pytorch.egnn_pytorch import EGNN_Network
+
+net = EGNN_Network(
+    num_tokens = 21,
+    dim = 32,
+    depth = 3,
+    edge_dim = 4,
+    num_nearest_neighbors = 3
+)
+
+feats = torch.randint(0, 21, (1, 1024))
+coors = torch.randn(1, 1024, 3)
+mask = torch.ones_like(feats).bool()
+
+continuous_edges = torch.randn(1, 1024, 1024, 4)
+
+# naive adjacency matrix
+# assuming the sequence is connected as a chain, with at most 2 neighbors - (1024, 1024)
+i = torch.arange(1024)
+adj_mat = (i[:, None] >= (i[None, :] - 1)) & (i[:, None] <= (i[None, :] + 1))
+
+feats_out, coors_out = net(feats, coors, edges = continuous_edges, mask = mask, adj_mat = adj_mat) # (1, 1024, 32), (1, 1024, 3)
+```
+
 ## Examples
 
 To run the protein backbone denoising example, first install `sidechainnet`
diff --git a/egnn_pytorch/egnn_pytorch.py b/egnn_pytorch/egnn_pytorch.py
index 5cad21d..85ac833 100644
--- a/egnn_pytorch/egnn_pytorch.py
+++ b/egnn_pytorch/egnn_pytorch.py
@@ -281,11 +281,12 @@ def __init__(
 
         self.token_emb = nn.Embedding(num_tokens, dim) if exists(num_tokens) else None
         self.edge_emb = nn.Embedding(num_edge_tokens, edge_dim) if exists(num_edge_tokens) else None
+        self.has_edges = edge_dim > 0
 
         self.num_adj_degrees = num_adj_degrees
         self.adj_emb = nn.Embedding(num_adj_degrees + 1, adj_dim) if exists(num_adj_degrees) and adj_dim > 0 else None
 
-        edge_dim = edge_dim if exists(num_edge_tokens) else 0
+        edge_dim = edge_dim if self.has_edges else 0
         adj_dim = adj_dim if exists(num_adj_degrees) else 0
 
         self.layers = nn.ModuleList([])
diff --git a/setup.py b/setup.py
index 3120ba5..2009c62 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
   name = 'egnn-pytorch',
   packages = find_packages(),
-  version = '0.1.1',
+  version = '0.1.2',
   license='MIT',
   description = 'E(n)-Equivariant Graph Neural Network - Pytorch',
   author = 'Phil Wang, Eric Alcaide',