make encoder attributes private

src/fast_forward/encoder.py

@@ -41,18 +41,20 @@ def __init__(
         :param **tokenizer_args: Additional tokenizer arguments.
         """
         super().__init__()
-        self.model = AutoModel.from_pretrained(model)
-        self.model.to(device)
-        self.model.eval()
-        self.tokenizer = AutoTokenizer.from_pretrained(model)
-        self.device = device
-        self.tokenizer_args = tokenizer_args
+        self._model = AutoModel.from_pretrained(model)
+        self._model.to(device)
+        self._model.eval()
+        self._tokenizer = AutoTokenizer.from_pretrained(model)
+        self._device = device
+        self._tokenizer_args = tokenizer_args
 
     def _encode(self, texts: "Sequence[str]") -> np.ndarray:
-        inputs = self.tokenizer(list(texts), return_tensors="pt", **self.tokenizer_args)
-        inputs.to(self.device)
+        inputs = self._tokenizer(
+            list(texts), return_tensors="pt", **self._tokenizer_args
+        )
+        inputs.to(self._device)
         with torch.no_grad():
-            return self.model(**inputs).pooler_output.detach().cpu().numpy()
+            return self._model(**inputs).pooler_output.detach().cpu().numpy()
 
 
 class LambdaEncoder(Encoder):
@@ -79,17 +81,17 @@ class TCTColBERTQueryEncoder(TransformerEncoder):
 
     def _encode(self, texts: "Sequence[str]") -> np.ndarray:
         max_length = 36
-        inputs = self.tokenizer(
+        inputs = self._tokenizer(
             ["[CLS] [Q] " + q + "[MASK]" * max_length for q in texts],
             max_length=max_length,
             truncation=True,
             add_special_tokens=False,
             return_tensors="pt",
-            **self.tokenizer_args,
+            **self._tokenizer_args,
         )
-        inputs.to(self.device)
+        inputs.to(self._device)
         with torch.no_grad():
-            embeddings = self.model(**inputs).last_hidden_state.detach().cpu().numpy()
+            embeddings = self._model(**inputs).last_hidden_state.detach().cpu().numpy()
             return np.average(embeddings[:, 4:, :], axis=-2)
 
 
@@ -102,18 +104,18 @@ class TCTColBERTDocumentEncoder(TransformerEncoder):
 
     def _encode(self, texts: "Sequence[str]") -> np.ndarray:
         max_length = 512
-        inputs = self.tokenizer(
+        inputs = self._tokenizer(
             ["[CLS] [D] " + text for text in texts],
             max_length=max_length,
             padding=True,
             truncation=True,
             add_special_tokens=False,
             return_tensors="pt",
-            **self.tokenizer_args,
+            **self._tokenizer_args,
         )
-        inputs.to(self.device)
+        inputs.to(self._device)
         with torch.no_grad():
-            outputs = self.model(**inputs)
+            outputs = self._model(**inputs)
             token_embeddings = outputs["last_hidden_state"][:, 4:, :]
             input_mask_expanded = (
                 inputs.attention_mask[:, 4:]