Merge pull request #2032 from Esri/jy-rvw-autodl

update format for displaying image and update markdown headings for nav

samples/04_gis_analysts_data_scientists/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.ipynb

@@ -39,7 +39,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# Introduction\n",
+ "## Introduction\n",
  "\n",
  "Crime analysis is an essential part of efficient law enforcement for any city. It involves:\n",
  "-\tCollecting data in a form that can be analyzed. \n",
@@ -62,7 +62,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# Prerequisites\n",
+ "## Prerequisites\n",
  "\n",
  "- **Data preparation** and **model training workflows** using `arcgis.learn` is based on [spaCy](https://spacy.io/usage/linguistic-features#named-entities) & [Hugging Face Transformers](https://huggingface.co/transformers/v3.0.2/index.html) libraries. A user can choose an appropriate backbone / library to train his/her model. \n",
  "- Refer to the section **\"Install deep learning dependencies of arcgis.learn module\"** [on this page](https://developers.arcgis.com/python/guide/install-and-set-up/#Install-deep-learning-dependencies) for detailed documentation on installation of the dependencies.\n",
@@ -76,7 +76,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# Necessary Imports"
+ "## Necessary Imports"
  ]
  },
  {
@@ -111,7 +111,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# Data preparation\n",
+ "## Data preparation\n",
  "\n",
  "Data preparation involves splitting the data into training and validation sets, creating the necessary data structures for loading data into the model and so on. The `prepare_data()` function can directly read the training samples in one of the above specified formats and automate the entire process."
  ]
@@ -396,7 +396,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# EntityRecognizer model\n",
+ "## EntityRecognizer model\n",
  "\n",
  "`EntityRecognizer` model in `arcgis.learn` can be used with spaCy's [EntityRecognizer](https://spacy.io/api/entityrecognizer) backbone or with [Hugging Face Transformers](https://huggingface.co/transformers/v3.0.2/index.html) backbones\n",
  "\n",
@@ -1280,7 +1280,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# Model Inference\n",
+ "## Model Inference\n",
  "\n",
  "Now we can use the trained model to extract entities from new text documents using `extract_entities()` method. This method expects the folder path of where new text document are located, or a list of text documents."
  ]
@@ -1478,7 +1478,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# Publishing the results as a feature layer\n",
+ "## Publishing the results as a feature layer\n",
  "\n",
  "The code below geocodes the extracted address and publishes the results as a feature layer."
  ]
@@ -1662,7 +1662,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# Create a hot spot map of crime densities\n",
+ "## Create a hot spot map of crime densities\n",
  "\n",
  "ArcGIS has a set of tools to help us identify, quantify and visualize spatial patterns in our data by identifying areas of statistically significant clusters.\n",
  "\n",
@@ -1737,7 +1737,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# Conclusion\n",
+ "## Conclusion\n",
  "\n",
  "This sample demonstrates how `EntityRecognizer()` from `arcgis.learn` can be used for information extraction from crime incident reports, which is an essential requirement for crime analysis. Then, we see how can this information be geocoded and visualized on a map for further analysis."
  ]
@@ -1746,7 +1746,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "# References\n",
+ "## References\n",
  "\n",
  "[1]: [Police Incident Reports(City of Madison)](https://www.cityofmadison.com/police/newsroom/incidentreports/)\n",
  "\n",
@@ -1764,9 +1764,9 @@
  "notebookRuntimeVersion": ""
  },
  "kernelspec": {
- "display_name": "Python [conda env:conda-dl] *",
+ "display_name": "Python 3 (ipykernel)",
  "language": "python",
- "name": "conda-env-conda-dl-py"
+ "name": "python3"
  },
  "language_info": {
  "codemirror_mode": {
@@ -1778,7 +1778,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.11.8"
+ "version": "3.11.0"
  }
  },
  "nbformat": 4,