remove html from titles

Nixtla · Nov 9, 2023 · a7d431b · a7d431b
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diff --git a/nbs/core.ipynb b/nbs/core.ipynb
@@ -28,7 +28,7 @@
    "id": "12fa25a4",
    "metadata": {},
    "source": [
-    "# <span style=\"color:DarkOrange\"> Core </span>\n",
+    "# Core\n",
     "> NeuralForecast contains two main components, PyTorch implementations deep learning predictive models, as well as parallelization and distributed computation utilities. The first component comprises low-level PyTorch model estimator classes like `models.NBEATS` and `models.RNN`. The second component is a high-level `core.NeuralForecast` wrapper class that operates with sets of time series data stored in pandas DataFrames."
    ]
   },

diff --git a/nbs/mint.json b/nbs/mint.json
@@ -70,7 +70,6 @@
       "pages": [
         "core.html",
         "models.html",
-        "models.html",
         {
           "group": "Models' Documentation",
           "pages": [

diff --git a/nbs/models.ipynb b/nbs/models.ipynb
@@ -119,7 +119,7 @@
    "id": "7ae65ca7",
    "metadata": {},
    "source": [
-    "# <span style=\"color:DarkOrange\"> Models </span>\n",
+    "# Models\n",
     "\n",
     "> NeuralForecast contains user-friendly implementations of neural forecasting models that allow for easy transition of computing capabilities (GPU/CPU), computation parallelization, and hyperparameter tuning.<br><br> All the NeuralForecast models are \"global\" because we train them with all the series from the input pd.DataFrame data `Y_df`, yet the optimization objective is, momentarily, \"univariate\" as it does not consider the interaction between the output predictions across time series. Like the StatsForecast library, `core.NeuralForecast` allows you to explore collections of models efficiently and contains functions for convenient wrangling of input and output pd.DataFrames predictions."
    ]