Rewrite TapasForQuestionAnswering wrapper

notebooks/266-table-question-answering/266-table-question-answering.ipynb

@@ -272,9 +272,17 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "class TapasForQuestionAnswering(model.__class__):  # it is better to keep the class name to avoid warnings\n",
+    "from transformers import TapasConfig\n",
+    "\n",
+    "\n",
+    "# get config for pretrained model\n",
+    "config = TapasConfig.from_pretrained('google/tapas-large-finetuned-wtq')\n",
+    "\n",
+    "\n",
+    "\n",
+    "class TapasForQuestionAnswering(TapasForQuestionAnswering):  # it is better to keep the class name to avoid warnings\n",
     "    def __init__(self, ov_model_path):\n",
-    "        super().__init__(model.config)  # pass config from the original model\n",
+    "        super().__init__(config)  # pass config from the pretrained model\n",
     "        self.tqa_model = core.compile_model(ov_model_path, device.value)\n",
     "        \n",
     "    def forward(self, input_ids, *, attention_mask, token_type_ids):\n",
@@ -325,10 +333,9 @@
     "\n",
     "\n",
     "def highlight_answers(x, coordinates):\n",
-    "    color = \"background-color: lightgreen\"\n",
     "    highlighted_table = pd.DataFrame('', index=x.index, columns=x.columns)\n",
     "    for coordinates_i in coordinates:\n",
-    "        highlighted_table.iloc[coordinates_i[0], coordinates_i[1]] = color\n",
+    "        highlighted_table.iloc[coordinates_i[0], coordinates_i[1]] = \"background-color: lightgreen\"\n",
     "    \n",
     "    return highlighted_table\n",
     "\n",