diff --git a/torchtext/models/roberta/model.py b/torchtext/models/roberta/model.py
index 60a4362550..4f68518b5e 100644
--- a/torchtext/models/roberta/model.py
+++ b/torchtext/models/roberta/model.py
@@ -10,6 +10,7 @@
 
 from .modules import (
     TransformerEncoder,
+    ProjectionLayer,
 )
 import logging
 logger = logging.getLogger(__name__)
@@ -25,6 +26,8 @@ class RobertaEncoderConf:
     num_attention_heads: int = 12
     num_encoder_layers: int = 12
     dropout: float = 0.1
+    projection_dim: Optional[int] = None
+    projection_dropout: Optional[float] = None
     scaling: Optional[float] = None
     normalize_before: bool = False
 
@@ -40,6 +43,8 @@ def __init__(
         num_attention_heads: int,
         num_encoder_layers: int,
         dropout: float = 0.1,
+        projection_dim: Optional[int] = None,
+        projection_dropout: Optional[float] = None,
         scaling: Optional[float] = None,
         normalize_before: bool = False,
     ):
@@ -62,6 +67,10 @@ def __init__(
             return_all_layers=False,
         )
 
+        self.project = nn.Identity()
+        if projection_dim is not None:
+            self.project = ProjectionLayer(embed_dim=embedding_dim, projection_dim=projection_dim, dropout=projection_dropout)
+
     @classmethod
     def from_config(cls, config: RobertaEncoderConf):
         return cls(**asdict(config))
@@ -73,6 +82,9 @@ def forward(self, tokens: Tensor, mask: Optional[Tensor] = None) -> Tensor:
         output = output.transpose(1, 0)
         if mask is not None:
             output = output[mask.to(torch.bool), :]
+
+        output = self.project(output)
+
         return output
 
 
diff --git a/torchtext/models/roberta/modules.py b/torchtext/models/roberta/modules.py
index 901e896270..0c3702d28b 100644
--- a/torchtext/models/roberta/modules.py
+++ b/torchtext/models/roberta/modules.py
@@ -31,6 +31,27 @@ def _make_positions(self, tensor, pad_index: int):
         return torch.cumsum(masked, dim=1) * masked + pad_index
 
 
+class ProjectionLayer(Module):
+    def __init__(self,
+                 embed_dim: int,
+                 projection_dim: int,
+                 dropout: Optional[float] = None) -> None:
+        super().__init__()
+
+        self.projection_layer = nn.Linear(embed_dim, projection_dim)
+        self.norm_layer = nn.LayerNorm(projection_dim)
+        if dropout is not None:
+            self.dropout_layer = nn.Dropout(dropout)
+        else:
+            self.dropout_layer = nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.projection_layer(x)
+        x = self.norm_layer(x)
+        x = self.dropout_layer(x)
+        return x
+
+
 class ResidualMLP(Module):
     def __init__(
         self,