diff --git a/torchtext/models/roberta/bundler.py b/torchtext/models/roberta/bundler.py
index dc4ce808d4..186a5ff688 100644
--- a/torchtext/models/roberta/bundler.py
+++ b/torchtext/models/roberta/bundler.py
@@ -56,11 +56,15 @@ class RobertaModelBundle:
     _head: Optional[Module] = None
     transform: Optional[Callable] = None
 
-    def get_model(self, head: Optional[Module] = None, load_weights=True, *, dl_kwargs=None) -> RobertaModel:
+    def get_model(self, head: Optional[Module] = None, load_weights: bool = True, freeze_encoder: bool = False, *, dl_kwargs=None) -> RobertaModel:
 
         if load_weights:
             assert self._path is not None, "load_weights cannot be True. The pre-trained model weights are not available for the current object"
 
+        if freeze_encoder:
+            if not load_weights or not self._path:
+                logger.warn("The encoder is not loaded with pre-trained weights. Setting freeze_encoder to True will hinder encoder from learning appropriate weights.")
+
         if head is not None:
             input_head = head
             if self._head is not None:
@@ -68,7 +72,7 @@ def get_model(self, head: Optional[Module] = None, load_weights=True, *, dl_kwar
         else:
             input_head = self._head
 
-        model = _get_model(self._params, input_head)
+        model = _get_model(self._params, input_head, freeze_encoder)
 
         if not load_weights:
             return model
diff --git a/torchtext/models/roberta/model.py b/torchtext/models/roberta/model.py
index 0656e647f8..1c9e291857 100644
--- a/torchtext/models/roberta/model.py
+++ b/torchtext/models/roberta/model.py
@@ -11,6 +11,8 @@
 from .modules import (
     TransformerEncoder,
 )
+import logging
+logger = logging.getLogger(__name__)
 
 
 @dataclass
@@ -103,6 +105,12 @@ def forward(self, tokens: Tensor) -> Tensor:
         return x
 
 
-def _get_model(params: RobertaEncoderParams, head: Module) -> RobertaModel:
+def _get_model(params: RobertaEncoderParams, head: Optional[Module] = None, freeze_encoder: bool = False) -> RobertaModel:
     encoder = RobertaEncoder(**asdict(params))
+    if freeze_encoder:
+        for param in encoder.parameters():
+            param.requires_grad = False
+
+        logger.info("Encoder weights are frozen")
+
     return RobertaModel(encoder, head)