diff --git a/flambe/field/text.py b/flambe/field/text.py
index 0a2ab81b..517c5fe8 100644
--- a/flambe/field/text.py
+++ b/flambe/field/text.py
@@ -1,4 +1,4 @@
-from typing import Optional, Dict
+from typing import Optional, Dict, Set
 from collections import OrderedDict as odict
 
 import torch
@@ -40,7 +40,8 @@ def __init__(self,  # nosec
                  embeddings: Optional[str] = None,
                  embeddings_format: str = 'glove',
                  embeddings_binary: bool = False,
-                 unk_init_all: bool = False) -> None:
+                 unk_init_all: bool = False,
+                 drop_unknown: bool = False) -> None:
         """Initialize the TextField.
 
         Parameters
@@ -77,6 +78,10 @@ def __init__(self,  # nosec
             If True, every token not provided in the input embeddings is
             given a random embedding from a normal distribution.
             Otherwise, all of them map to the '<unk>' token.
+        drop_unknown: bool
+            Whether to drop tokens that don't have embeddings
+            associated. Defaults to True.
+            Important: this flag will only work when using embeddings.
 
         """
         self.tokenizer = tokenizer or WordTokenizer()
@@ -92,6 +97,9 @@ def __init__(self,  # nosec
         self.embeddings_binary = embeddings_binary
         self.embedding_matrix: Optional[torch.Tensor] = None
         self.unk_init_all = unk_init_all
+        self.drop_unknown = drop_unknown
+
+        self.unk_numericals: Set[int] = set()
 
         self.vocab: Dict = odict()
         specials = [pad_token, unk_token, sos_token, eos_token]
@@ -169,13 +177,16 @@ def setup(self, *data: np.ndarray) -> None:
                         if token in model:
                             self.vocab[token] = index = index + 1
                             embeddings_matrix.append(torch.tensor(model[token]))
-                        elif self.unk_init_all:
-                            # Give every OOV it's own embedding
-                            self.vocab[token] = index = index + 1
-                            embeddings_matrix.append(torch.randn(model.vector_size))
                         else:
-                            # Collapse all OOV's to the same token id
-                            self.vocab[token] = self.vocab[self.unk]
+                            if self.unk_init_all:
+                                # Give every OOV it's own embedding
+                                self.vocab[token] = index = index + 1
+                                embeddings_matrix.append(torch.randn(model.vector_size))
+                            else:
+                                # Collapse all OOV's to the same token
+                                # id
+                                self.vocab[token] = self.vocab[self.unk]
+                            self.unk_numericals.add(self.vocab[token])
                     else:
                         self.vocab[token] = index = index + 1
 
@@ -219,6 +230,12 @@ def process(self, example: str) -> torch.Tensor:  # type: ignore
                     token = self.unk
 
             numerical = self.vocab[token]  # type: ignore
+
+            if self.drop_unknown and \
+                    self.embeddings is not None and numerical in self.unk_numericals:
+                # Don't add unknown tokens in case the flag is activated
+                continue
+
             numericals.append(numerical)
 
         return torch.tensor(numericals).long()
diff --git a/requirements.txt b/requirements.txt
index 3e491154..b90ca692 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -12,7 +12,7 @@ requests>=2.21.0
 Flask>=1.0
 tensorboardx-hparams>=1.7
 GitPython>=2.1.11
-sru>=2.1.6
+sru>=2.1.7
 psutil>=5.6.0
 pygments>=2.3.1
 nltk>=3.4.1