diff --git a/pyproject.toml b/pyproject.toml
index 41fd802..45add48 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,7 +9,7 @@ profile = "black"
 
 [project]
 name = "turftopic"
-version = "0.23.1"
+version = "0.23.2"
 description = "Topic modeling with contextual representations from sentence transformers."
 authors = [
    { name = "Márton Kardos <power.up1163@gmail.com>", email = "martonkardos@cas.au.dk" }
diff --git a/turftopic/models/_snmf.py b/turftopic/models/_snmf.py
index 7b9b016..37efd47 100644
--- a/turftopic/models/_snmf.py
+++ b/turftopic/models/_snmf.py
@@ -54,6 +54,7 @@ def update_G(X, G, F, sparsity=0):
     denominator = jnp.maximum(denominator, EPSILON)
     delta_G = jnp.sqrt(numerator / denominator)
     G *= delta_G
+    G = G / jnp.linalg.norm(G)
     return G
 
 
@@ -128,7 +129,24 @@ def fit_timeslice(self, X_t: np.ndarray, G_t: np.ndarray):
         return F.T
 
     def transform(self, X: np.ndarray):
-        G = jnp.maximum(X @ jnp.linalg.pinv(self.components_), 0)
+        G = init_G(
+            X.T,
+            n_components=self.n_components,
+            random_state=self.random_state,
+        )
+        F = self.components_.T
+        update = jit(lambda G: update_G(X.T, G, F, sparsity=self.sparsity))
+        error_at_init = rec_err(X.T, F, G)
+        prev_error = error_at_init
+        for i in range(self.max_iter):
+            G = update(G)
+            err = rec_err(X.T, F, G)
+            if (err < error_at_init) and (
+                (prev_error - err) / error_at_init
+            ) < self.tol:
+                if self.verbose:
+                    print(f"Converged after {i} iterations")
+                break
         return np.array(G)
 
     def inverse_transform(self, X):
diff --git a/turftopic/models/senstopic.py b/turftopic/models/senstopic.py
index cadc37a..42cc44e 100644
--- a/turftopic/models/senstopic.py
+++ b/turftopic/models/senstopic.py
@@ -205,6 +205,11 @@ def fit_transform(
             console.log("Model fitting done.")
         return doc_topic
 
+    def transform(self, raw_documents, embeddings=None):
+        if embeddings is None:
+            embeddings = self.encoder_.encode(raw_documents)
+        return self.decomposition.transform(embeddings)
+
     def fit_transform_multimodal(
         self,
         raw_documents: list[str],