made changes to the prerequisites section of the sample notebook
"source": [
"# Prerequisites\n",
"\n",
- "- Data preparation and model training workflows using arcgis.learn have a dependency on spaCy. 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-arcgis.learn-module) for detailed documentation on installation of the dependencies.\n",
- "- Labelled data: In order for Entity Recognizer to learn, it needs to see examples that have been labelled for all the custom categories that the model is expected to extract. Labelled data for this sample notebook is located at data/EntityRecognizer/labelled_crime_reports.json\n",
- "- To learn how to use Doccano[[2]](#References) for labelling text, please see the guide on [Labeling text using Doccano](https://developers.arcgis.com/python/sample-notebooks/labeling-text-using-doccano)\n",
- "- Test documents to extract named entities are in a zipped file at data/EntityRecognizer/reports.zip\n",
- "- To learn more on how `EntityRecognizer` works, please see the guide on [Named Entity Extraction Workflow with arcgis.learn](https://developers.arcgis.com/python/sample-notebooks/how-named-entity-recognition-works/)."
+ "- **Data preparation** and **model training workflows** using `arcgis.learn` is based on [spaCy](https://spacy.io/usage/linguistic-features#named-entities) & [Hugging Face Transformer](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-arcgis.learn-module) for detailed documentation on installation of the dependencies.\n",
+ "- **Labelled data**: In order for `EntityRecognizer` to learn, it needs to see examples that have been labelled for all the custom categories that the model is expected to extract. Labelled data for this sample notebook is located at `data/EntityRecognizer/labelled_crime_reports.json`.\n",
+ "- To learn how to use **Doccano**[[2]](#References) for labelling text, please see the guide on [Labeling text using Doccano](https://developers.arcgis.com/python/sample-notebooks/labeling-text-using-doccano).\n",
+ "- Test documents to extract named entities are in a zipped file at `data/EntityRecognizer/reports.zip`.\n",
+ "- To learn more on how `EntityRecognizer` works, please see the guide on [Named Entity Extraction Workflow with arcgis.learn](https://developers.arcgis.com/python/guide/how-named-entity-recognition-works/)."
]
},
{
"source": [
"# 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()` method can directly read the training samples in one of the above specified formats and automate the entire process."
+ "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."
]
},
{
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.12"
+ "version": "3.6.9"
}
},
"nbformat": 4,
