Merge pull request #170 from pandora-s-git/main

OCR Update

Author: pandora-s-git

mistral/ocr/batch_ocr.ipynb

@@ -23,7 +23,7 @@
         "### Used\n",
         "\n",
         "- OCR\n",
-        "- Batch Inference"
+        "- Batch Inference\n"
       ]
     },
     {
@@ -571,4 +571,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

mistral/ocr/data_extraction.ipynb

@@ -10,16 +10,20 @@
         "\n",
         "---\n",
         "\n",
-        "## OCR Exploration and Structured Outputs\n",
-        "In this cookbook, we will explore the basics of OCR and leverage it together with existing models to achieve structured outputs fueled by our OCR model.\n",
+        "## OCR Exploration and Simple Structured Outputs (Deprecated)\n",
+        "In this cookbook, we will explore the basics of OCR and leverage it together with existing models to achieve structured outputs fueled by our OCR model (we recommend using the new Annotations feature instead for better results).\n",
         "\n",
         "You may want to do this in case current vision models are not powerful enough, hence enhancing their vision OCR capabilities with the OCR model to achieve better structured data extraction.\n",
         "\n",
         "---\n",
         "\n",
         "### Model Used\n",
         "- Mistral OCR\n",
-        "- Pixtral 12B & Ministral 8B\n"
+        "- Pixtral 12B & Ministral 8B\n",
+        "\n",
+        "---\n",
+        "\n",
+        "**For a more up to date guide on structured outputs visit our [Annotations cookbook](https://github.com/mistralai/cookbook/blob/main/mistral/ocr/data_extraction.ipynb) on Data Extraction.**\n"
       ]
     },
     {
@@ -35,7 +39,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
         "id": "po7Cukllt8za"
       },
@@ -56,7 +60,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "id": "MtKgrASwF3Ol"
       },
@@ -80,7 +84,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
         "id": "odfkuCk6qSAw"
       },
@@ -108,7 +112,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -175,7 +179,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -255,7 +259,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -328,7 +332,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -456,7 +460,7 @@
         "id": "1m19STu2DDfI",
         "outputId": "06f99dfe-b697-4d82-bf20-0fa60435d47f"
       },
-      "execution_count": 8,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -503,7 +507,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": null,
       "metadata": {
         "id": "oM2ensmIwh4H"
       },
@@ -584,7 +588,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -656,7 +660,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -709,4 +713,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}