<commit_before>---
layout: model
title: Relation Extraction Model Clinical
author: <NAME>
name: re_drug_drug_interaction_clinical
class: RelationExtractionModel
language: en
repository: clinical/models
date: 2020-09-03
tags: [clinical,licensed,relation extraction,en]
article_header:
   type: cover
use_language_switcher: "Python-Scala-Java"
---

{:.h2_title}
## Description
Relation Extraction model based on syntactic features using deep learning. This model can be used to identify drug-drug interactions relationships among drug entities.

## Included Relations
``DDI-advise``, ``DDI-effect``, ``DDI-mechanism``, ``DDI-int``, ``DDI-false``.

{:.btn-box}
<button class="button button-orange" disabled>Live Demo</button>
[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/10.Clinical_Relation_Extraction.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_drug_drug_interaction_clinical_en_2.5.5_2.4_1599156924424.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
{:.h2_title}
## How to use 
<div class="tabs-box" markdown="1">

{% include programmingLanguageSelectScalaPython.html %}

```python
...
ddi_re_model = RelationExtractionModel.pretrained("re_drug_drug_interaction_clinical","en","clinical/models")\<commit_msg>[skip_ci] new metadata lines added to 158.file 2020-09-03-re_drug_drug_interaction_clinical_en.md<commit_after>---
layout: model
title: Relation Extraction Model Clinical
author: <NAME>
name: re_drug_drug_interaction_clinical
class: RelationExtractionModel
language: en
repository: clinical/models
date: 2020-09-03
task: Relation Extraction
edition: Spark NLP for Healthcare 2.5.5
tags: [clinical,licensed,relation extraction,en]
article_header:
   type: cover
use_language_switcher: "Python-Scala-Java"
---

{:.h2_title}
## Description
Relation Extraction model based on syntactic features using deep learning. This model can be used to identify drug-drug interactions relationships among drug entities.

## Included Relations
``DDI-advise``, ``DDI-effect``, ``DDI-mechanism``, ``DDI-int``, ``DDI-false``.

{:.btn-box}
<button class="button button-orange" disabled>Live Demo</button>
[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/10.Clinical_Relation_Extraction.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_drug_drug_interaction_clinical_en_2.5.5_2.4_1599156924424.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
{:.h2_title}
## How to use 
<div class="tabs-box" markdown="1">

{% include programmingLanguageSelectScalaPython.html %}

```python
...
ddi_re_model = RelationExtractionModel.pretrained("re_drug_drug_interaction_clinical","en","clinical/models")\
{'fraction_non_alphanumeric': 0.13415446071904127, 'fraction_numerical': 0.02596537949400799, 'mean_word_length': 6.784974093264249, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, 'https://': 4, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5106095', 'n_tokens_mistral': 1129, 'n_tokens_neox': 985, 'n_words': 177}