<commit_before>---
layout: model
title: Detect PHI for Deidentification purposes (Spanish)
author: <NAME>
name: ner_deid_subentity_roberta
date: 2022-01-17
tags: [deid, es, licensed]
task: De-identification
language: es
edition: Spark NLP for Healthcare 3.3.4
spark_version: 3.0
supported: true
article_header:
  type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Named Entity Recognition annotators allow for a generic model to be trained by using a Deep Learning architecture (Char CNNs - BiLSTM - CRF - word embeddings) inspired on a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM,CNN. 

Deidentification NER (Spanish) is a Named Entity Recognition model that annotates text to find protected health information that may need to be de-identified. It detects 13 entities. This NER model is trained with a combination of custom datasets, Spanish 2002 conLL, MeddoProf dataset and several data augmentation mechanisms

## Predicted Entities<commit_msg>Adds info about the embeddings to the model<commit_after>---
layout: model
title: Detect PHI for Deidentification purposes (Spanish, Roberta embeddings)
author: <NAME>
name: ner_deid_subentity_roberta
date: 2022-01-17
tags: [deid, es, licensed]
task: De-identification
language: es
edition: Spark NLP for Healthcare 3.3.4
spark_version: 3.0
supported: true
article_header:
  type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Named Entity Recognition annotators allow for a generic model to be trained by using a Deep Learning architecture (Char CNNs - BiLSTM - CRF - word embeddings) inspired on a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM,CNN. 

Deidentification NER (Spanish) is a Named Entity Recognition model that annotates text to find protected health information that may need to be de-identified. It detects 13 entities. This NER model is trained with a combination of custom datasets, Spanish 2002 conLL, MeddoProf dataset and several data augmentation mechanisms. This model uses Roberta Clinical Embeddings.

## Predicted Entities
{'fraction_non_alphanumeric': 0.05874190564292322, 'fraction_numerical': 0.01757631822386679, 'mean_word_length': 5.144886363636363, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7061130', 'n_tokens_mistral': 653, 'n_tokens_neox': 587, 'n_words': 280}