Create pipeline.py

pipeline.py

@@ -0,0 +1,21 @@
+from typing import List
+
+import os
+import fasttext.util
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        """
+        Initialize model
+        """
+        self.model = fasttext.load_model(os.path.join(path, 'ubertext.fiction_news_wikipedia.filter_rus+short.tokens.txt.algo-skipgram.epochs-15.subwords-2..5.wordngram-3.neg_sampling-15'))
+
+    def __call__(self, inputs: str) -> List[float]:
+        """
+        Args:
+            inputs (:obj:`str`):
+                a string to get the features of.
+        Return:
+            A :obj:`list` of floats: The features computed by the model.
+        """
+        return self.model.get_sentence_vector(inputs).tolist()