Fix progress bar

model2vec/model.py

@@ -162,7 +162,7 @@ def encode_as_sequence(
         sentences: list[str] | str,
         max_length: int | None = None,
         batch_size: int = 1024,
-        show_progressbar: bool = False,
+        show_progress_bar: bool = False,
     ) -> list[np.ndarray] | np.ndarray:
         """
         Encode a list of sentences as a list of numpy arrays of tokens.
@@ -177,7 +177,7 @@ def encode_as_sequence(
         :param max_length: The maximum length of the sentences. Any tokens beyond this length will be truncated.
             If this is None, no truncation is done.
         :param batch_size: The batch size to use.
-        :param show_progressbar: Whether to show the progress bar.
+        :param show_progress_bar: Whether to show the progress bar.
         :return: The encoded sentences with an embedding per token.
         """
         was_single = False
@@ -189,7 +189,7 @@ def encode_as_sequence(
         for batch in tqdm(
             self._batch(sentences, batch_size),
             total=math.ceil(len(sentences) / batch_size),
-            disable=not show_progressbar,
+            disable=not show_progress_bar,
         ):
             out_array.extend(self._encode_batch_as_sequence(batch, max_length))
 
@@ -213,7 +213,7 @@ def _encode_batch_as_sequence(self, sentences: list[str], max_length: int | None
     def encode(
         self,
         sentences: list[str] | str,
-        show_progressbar: bool = False,
+        show_progress_bar: bool = False,
         max_length: int | None = 512,
         batch_size: int = 1024,
         **kwargs: Any,
@@ -225,7 +225,7 @@ def encode(
         For ease of use, we don't batch sentences together.
 
         :param sentences: The list of sentences to encode. You can also pass a single sentence.
-        :param show_progressbar: Whether to show the progress bar.
+        :param show_progress_bar: Whether to show the progress bar.
         :param max_length: The maximum length of the sentences. Any tokens beyond this length will be truncated.
             If this is None, no truncation is done.
         :param batch_size: The batch size to use.
@@ -241,7 +241,7 @@ def encode(
         for batch in tqdm(
             self._batch(sentences, batch_size),
             total=math.ceil(len(sentences) / batch_size),
-            disable=not show_progressbar,
+            disable=not show_progress_bar,
         ):
             out_arrays.append(self._encode_batch(batch, max_length))
 

tutorials/semantic_deduplication.ipynb

@@ -110,7 +110,7 @@
    ],
    "source": [
     "# Encode texts into embeddings\n",
-    "embedding_matrix = model.encode(texts, show_progressbar=True)"
+    "embedding_matrix = model.encode(texts)"
    ]
   },
   {
@@ -407,8 +407,8 @@
     "texts_test = ds_test['text']\n",
     "\n",
     "# Encode texts into embeddings\n",
-    "embedding_matrix_train = model.encode(texts_train, show_progressbar=True)\n",
-    "embedding_matrix_test = model.encode(texts_test, show_progressbar=True)\n",
+    "embedding_matrix_train = model.encode(texts_train)\n",
+    "embedding_matrix_test = model.encode(texts_test)\n",
     "\n",
     "def deduplicate_across_datasets(embedding_matrix_1: np.ndarray, embedding_matrix_2: np.ndarray, threshold: float, batch_size: int = 1024) -> tuple[list[int], dict[int, int]]:\n",
     "    \"\"\"\n",