diff --git a/gfun/vgfs/textualTransformerGen.py b/gfun/vgfs/textualTransformerGen.py
index 8f8d661..70e9909 100644
--- a/gfun/vgfs/textualTransformerGen.py
+++ b/gfun/vgfs/textualTransformerGen.py
@@ -6,7 +6,9 @@ from collections import defaultdict
 
 import numpy as np
 import torch
+import torch.nn as nn
 import transformers
+from transformers import MT5EncoderModel
 from torch.utils.data import Dataset
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
@@ -17,6 +19,24 @@ from gfun.vgfs.viewGen import ViewGen
 transformers.logging.set_verbosity_error()
 
 
+class MT5ForSequenceClassification(nn.Module):
+    def __init__(self, model_name, num_labels, output_hidden_states):
+        super().__init__()
+
+        self.mt5encoder = MT5EncoderModel.from_pretrained(
+            model_name, output_hidden_states=output_hidden_states
+        )
+        self.dropout = nn.Dropout(0.1)
+        self.linear = nn.Linear(512, num_labels)
+
+    def forward(self, input_ids, attn_mask):
+        # TODO: output hidden states
+        outputs = self.mt5encoder(input_ids=input_ids, attention_mask=attn_mask)
+        outputs = self.dropout(outputs[0])
+        outputs = self.linear(outputs)
+        return outputs
+
+
 class TextualTransformerGen(ViewGen, TransformerGen):
     def __init__(
         self,
@@ -65,13 +85,20 @@ class TextualTransformerGen(ViewGen, TransformerGen):
             return "bert-base-multilingual-uncased"
         elif "xlm" == model_name:
             return "xlm-roberta-base"
+        elif "mt5" == model_name:
+            return "google/mt5-small"
         else:
             raise NotImplementedError
 
     def load_pretrained_model(self, model_name, num_labels):
-        return AutoModelForSequenceClassification.from_pretrained(
-            model_name, num_labels=num_labels, output_hidden_states=True
-        )
+        if model_name == "google/mt5-small":
+            return MT5ForSequenceClassification(
+                model_name, num_labels=num_labels, output_hidden_states=True
+            )
+        else:
+            return AutoModelForSequenceClassification.from_pretrained(
+                model_name, num_labels=num_labels, output_hidden_states=True
+            )
 
     def load_tokenizer(self, model_name):
         return AutoTokenizer.from_pretrained(model_name)