Expose the trainer to Python bindings.

bindings/python/py_src/tokenizers/implementations/sentencepiece_unigram.py

@@ -40,3 +40,20 @@ class SentencePieceUnigramTokenizer(BaseTokenizer):
         }
 
         super().__init__(tokenizer, parameters)
+
+    def train(
+        self,
+        files: Union[str, List[str]],
+        vocab_size: int = 8000,
+        show_progress: bool = True,
+        special_tokens: List[Union[str, AddedToken]] = [],
+    ):
+        """ Train the model using the given files """
+
+        trainer = trainers.UnigramTrainer(
+            vocab_size=vocab_size, special_tokens=special_tokens, show_progress=show_progress,
+        )
+
+        if isinstance(files, str):
+            files = [files]
+        self._tokenizer.train(trainer, files)

bindings/python/py_src/tokenizers/trainers/__init__.py

@@ -3,3 +3,4 @@ from .. import trainers
 Trainer = trainers.Trainer
 BpeTrainer = trainers.BpeTrainer
 WordPieceTrainer = trainers.WordPieceTrainer
+UnigramTrainer = trainers.UnigramTrainer

bindings/python/py_src/tokenizers/trainers/__init__.pyi

@@ -111,3 +111,32 @@ class WordPieceTrainer(Trainer):
             Trainer
         """
         pass
+
+class UnigramTrainer(Trainer):
+    """ UnigramTrainer
+
+    Capable of training a Unigram model
+    """
+
+    def __init__(
+        self,
+        vocab_size: int = 8000,
+        show_progress: bool = True,
+        special_tokens: List[Union[str, AddedToken]] = [],
+    ) -> Trainer:
+        """ Instantiate a new UnigramTrainer with the given options:
+
+        Args:
+            vocab_size: unsigned int:
+                The size of the final vocabulary, including all tokens and alphabet.
+
+            show_progress: boolean:
+                Whether to show progress bars while training.
+
+            special_tokens: List[Union[str, AddedToken]]:
+                A list of special tokens the model should know of.
+
+        Returns:
+            Trainer
+        """
+        pass