RelCAT tutorials v1

.github/workflows/main.yml

@@ -9,7 +9,7 @@ on:
 jobs:
   main:
 
-    runs-on: macos-latest
+    runs-on: ubuntu-24.04
     strategy:
       matrix:
         part: [
@@ -18,6 +18,7 @@ jobs:
           introductory/Part_4_*.ipynb,
           introductory/Part_5_*.ipynb,
           introductory/Part_1_*.ipynb,
+          introductory/Part_6_*.ipynb,
           specialised/Comparing_Models_with_RegressionSuite.ipynb  # this should work still
           # specialised/*.ipynb         # To make it run, the SnomedCT file needs to be mocked
         ]

README.md

@@ -18,7 +18,8 @@ In this tutorial, we will walk you through each stage of a basic MedCAT project.
 | 4.2  | [Supervised training and fine-tuning + Meta-annotations](https://htmlpreview.github.io/?https://github.com/CogStack/MedCATtutorials/blob/main/notebooks/introductory/Part_4_2_Supervised_Training_and_Meta_annotations.html)                      | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/CogStack/MedCATtutorials/blob/main/notebooks/introductory/Part_4_2_Supervised_Training_and_Meta_annotations.ipynb) | -         |
 | 4.3  | [Annotating documents with the full MedCAT pipeline with MetaAnnotations](https://htmlpreview.github.io/?https://github.com/CogStack/MedCATtutorials/blob/main/notebooks/introductory/Part_4_3_Annotating_documents_with_the_full_MedCAT_pipeline_with_MetaAnnotations.html)     | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/CogStack/MedCATtutorials/blob/main/notebooks/introductory/Part_4_3_Annotating_documents_with_the_full_MedCAT_pipeline_with_MetaAnnotations.ipynb) | -         |
 | 5    | [Analysing the results](https://htmlpreview.github.io/?https://github.com/CogStack/MedCATtutorials/blob/main/notebooks/introductory/Part_5_Prevalence_of_Physical_and_Mental_Diseases.html)                                                       | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/CogStack/MedCATtutorials/blob/main/notebooks/introductory/Part_5_Prevalence_of_Physical_and_Mental_Diseases.ipynb) | [TDS](https://medium.com/@w_is_h/prevalence-of-physical-and-mental-diseases-450c0f4f5851)         |
-
+| 6.1  | [Supervised training Relation-annotations](https://htmlpreview.github.io/?https://github.com/CogStack/MedCATtutorials/blob/rel_cat_tutorials/notebooks/introductory/Part_6_1_Supervised_Training_Relation_Extraction.html) | - | - |
+| 6.2  | [Infering relationships from annotations](https://htmlpreview.github.io/?https://github.com/CogStack/MedCATtutorials/blob/rel_cat_tutorials/notebooks/introductory/Part_6_2_Infering_relations_from_annotations_with_Relation_toolkit.html) | - | - |
 
 ## Specialised tutorials
 

notebooks/introductory/Part_1_1_OPTIONAL_Logging_With_MedCAT.html

@@ -13095,7 +13095,7 @@ <h1 id="MedCAT-tutorial---logging-with-MedCAT">MedCAT tutorial - logging with Me
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Install medcat</span>
-<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.14.0
+<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.16.0
 <span class="k">try</span><span class="p">:</span>
     <span class="kn">from</span> <span class="nn">medcat.cat</span> <span class="kn">import</span> <span class="n">CAT</span>
 <span class="k">except</span><span class="p">:</span>

notebooks/introductory/Part_1_1_OPTIONAL_Logging_With_MedCAT.ipynb

@@ -19,7 +19,7 @@
    "outputs": [],
    "source": [
     "# Install medcat\n",
-    "! pip install medcat~=1.14.0\n",
+    "! pip install medcat~=1.16.0\n",
     "try:\n",
     "    from medcat.cat import CAT\n",
     "except:\n",
@@ -189,7 +189,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.6 ('venv': venv)",
+   "display_name": "tvenv",
    "language": "python",
    "name": "python3"
   },
@@ -204,11 +204,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "0d20877e080635f7c4968c49fce8aa5e74c8d7837295e20d04554edce953f9c6"
-   }
   }
  },
  "nbformat": 4,

notebooks/introductory/Part_3_1_Building_a_Concept_Database_and_Vocabulary.html

@@ -13095,11 +13095,11 @@ <h3 id="First-we-need-to-install-MedCAT">First we need to install MedCAT<a class
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Install MedCAT</span>
-<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.14.0
+<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.16.0
 <span class="c1"># Get the scispacy model</span>
 <span class="o">!</span> python -m spacy download en_core_web_md
 <span class="k">try</span><span class="p">:</span>

notebooks/introductory/Part_3_1_Building_a_Concept_Database_and_Vocabulary.ipynb

@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -322,7 +322,7 @@
    ],
    "source": [
     "# Install MedCAT\n",
-    "! pip install medcat~=1.14.0\n",
+    "! pip install medcat~=1.16.0\n",
     "# Get the scispacy model\n",
     "! python -m spacy download en_core_web_md\n",
     "try:\n",
@@ -1371,7 +1371,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.8.5 ('.venv': venv)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1386,11 +1386,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "60954f76b319195d8b66f263176ecf047c3a086773bff2d42e03144b47421836"
-   }
   }
  },
  "nbformat": 4,

notebooks/introductory/Part_3_2_Extracting_Diseases_from_Electronic_Health_Records.html

@@ -13088,11 +13088,11 @@ <h1 id="Now-let's-start-extracting-concepts-from-unstructured-text!">Now let's s
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Install medcat</span>
-<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.14.0
+<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.16.0
 <span class="c1"># install seaborn</span>
 <span class="o">!</span> pip install seaborn
 <span class="k">try</span><span class="p">:</span>
@@ -14706,10 +14706,10 @@ <h2 id="Use-Multiprocessing">Use Multiprocessing<a class="anchor-link" href="#Us
 
 
 
-<div id="9de888b0-4c6d-44f7-bd70-b4316f888f78"></div>
+<div id="22300e60-822a-4878-b1d9-b57e2581a4b9"></div>
 <div class="output_subarea output_widget_view ">
 <script type="text/javascript">
-var element = $('#9de888b0-4c6d-44f7-bd70-b4316f888f78');
+var element = $('#22300e60-822a-4878-b1d9-b57e2581a4b9');
 </script>
 <script type="application/vnd.jupyter.widget-view+json">
 {"model_id": "05b18c97da9d4d05b9280df006a5fb82", "version_major": 2, "version_minor": 0}

notebooks/introductory/Part_3_2_Extracting_Diseases_from_Electronic_Health_Records.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -275,7 +275,7 @@
    ],
    "source": [
     "# Install medcat\n",
-    "! pip install medcat~=1.14.0\n",
+    "! pip install medcat~=1.16.0\n",
     "# install seaborn\n",
     "! pip install seaborn\n",
     "try:\n",
@@ -2847,7 +2847,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.8.5 ('.venv': venv)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -2862,11 +2862,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "60954f76b319195d8b66f263176ecf047c3a086773bff2d42e03144b47421836"
-   }
   }
  },
  "nbformat": 4,

notebooks/introductory/Part_3_3_Model_technical_optimisations.html

@@ -13088,11 +13088,11 @@ <h1 id="Now-let's-look-at-ways-to-optimise-the-model-for-our-specific-use-case">
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Install medcat</span>
-<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.14.0
+<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.16.0
 <span class="k">try</span><span class="p">:</span>
     <span class="kn">from</span> <span class="nn">medcat.cat</span> <span class="kn">import</span> <span class="n">CAT</span>
 <span class="k">except</span><span class="p">:</span>

notebooks/introductory/Part_3_3_Model_technical_optimisations.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -165,7 +165,7 @@
    ],
    "source": [
     "# Install medcat\n",
-    "! pip install medcat~=1.14.0\n",
+    "! pip install medcat~=1.16.0\n",
     "try:\n",
     "    from medcat.cat import CAT\n",
     "except:\n",
@@ -413,7 +413,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.8.5 ('.venv': venv)",
+   "display_name": "tvenv",
    "language": "python",
    "name": "python3"
   },
@@ -428,11 +428,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "60954f76b319195d8b66f263176ecf047c3a086773bff2d42e03144b47421836"
-   }
   }
  },
  "nbformat": 4,

notebooks/introductory/Part_4_1_ByteLevelBPETokenizer_and_Embeddings.html

@@ -13089,11 +13089,11 @@ <h4><b>NOTE:</b> This tutorial is applicable for BiLSTM model not BERT as BERT m
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Install medcat</span>
-<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.14.0
+<span class="o">!</span> pip install medcat~<span class="o">=</span><span class="m">1</span>.16.0
 <span class="k">try</span><span class="p">:</span>
     <span class="kn">from</span> <span class="nn">medcat.cat</span> <span class="kn">import</span> <span class="n">CAT</span>
 <span class="k">except</span><span class="p">:</span>

notebooks/introductory/Part_4_1_ByteLevelBPETokenizer_and_Embeddings.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -272,7 +272,7 @@
    ],
    "source": [
     "# Install medcat\n",
-    "! pip install medcat~=1.14.0\n",
+    "! pip install medcat~=1.16.0\n",
     "try:\n",
     "    from medcat.cat import CAT\n",
     "except:\n",
@@ -678,7 +678,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.8.5 ('.venv': venv)",
+   "display_name": "tvenv",
    "language": "python",
    "name": "python3"
   },
@@ -693,11 +693,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "60954f76b319195d8b66f263176ecf047c3a086773bff2d42e03144b47421836"
-   }
   }
  },
  "nbformat": 4,