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  • John Snow Labs
  • Nov 01, 2021

Roberta Clinical Word Embeddings (Spanish)

  • embeddings
  • spanish
  • biomedical
  • clinical
  • roberta
  • es

Description

Biomedical pretrained language model for Spanish with a 768 embeddings dimension, imported from Hugging Face (https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es) to be used in SparkNLP using RobertaEmbeddings() transfformer class.

This model is a RoBERTa-based model trained on a biomedical corpus in Spanish collected from several sources (see dataset section). The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences.

To see more details, please check the official page in Hugging Face: https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es

Predicted Entities

Copy S3 URI

How to use

documentAssembler = nlp.DocumentAssembler()\
.setInputCol("term")\
.setOutputCol("document")

tokenizer = nlp.Tokenizer()\
.setInputCols("document")\
.setOutputCol("token")

roberta_embeddings = nlp.RoBertaEmbeddings.pretrained("roberta_base_biomedical", "es")\
.setInputCols(["document", "token"])\
.setOutputCol("roberta_embeddings")

pipeline = Pipeline(stages = [
documentAssembler,
tokenizer,
roberta_embeddings])

val documentAssembler = new DocumentAssembler()
.setInputCol("term")
.setOutputCol("document")

val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")

val roberta_embeddings = RoBertaEmbeddings.pretrained("roberta_base_biomedical", "es")
.setInputCols(Array("document", "token"))
.setOutputCol("roberta_embeddings")

val pipeline = new Pipeline().setStages(Array(
documentAssembler,
tokenizer,
roberta_embeddings))

import nlu
nlu.load("es.embed.roberta_base_biomedical").predict("""Put your text here.""")

Results

The model has been evaluated on the Named Entity Recognition (NER) using the following datasets (taken from https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es)

* PharmaCoNER: is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/).

* CANTEMIST: is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ).

* ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables.

Model Information

Model Name: roberta_base_biomedical
Compatibility: Spark NLP 3.3.0+
License: Licensed
Edition: Official
Input Labels: [document, token]
Output Labels: [embeddings]
Language: es

Data Source

Datasets are available in the official author(s) github project, available here: https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es, and include:

  • Medical crawler 745,705,946 Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains.

  • Clinical cases misc. 102,855,267 A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document.

  • Clinical notes/documents 91,250,080 Collection of more than 278K clinical documents, including discharge reports, clinical course notes and X-ray reports, for a total of 91M tokens.

  • Scielo 60,007,289 Publications written in Spanish crawled from the Spanish SciELO server in 2017.

  • BARR2_background 24,516,442 Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines.

  • Wikipedia_life_sciences 13,890,501 Wikipedia articles crawled 04/01/2021 with the Wikipedia API python library starting from the “Ciencias_de_la_vida” category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content.

  • Patents 13,463,387 Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: “A61B”, “A61C”,”A61F”, “A61H”, “A61K”, “A61L”,”A61M”, “A61B”, “A61P”.

  • EMEA 5,377,448 Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency.

  • mespen_Medline 4,166,077 Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources are aggregated from the MedlinePlus source.

  • PubMed 1,858,966 Open-access articles from the PubMed repository crawled in 2017.

Benchmarking

Taken from https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es:

Task/models   F1 | Precision | Recall
PharmaCoNER   90.04 | 88.92 | 91.18
CANTEMIST     83.34 | 81.48 | 85.30
ICTUSnet      88.08 | 84.92 | 91.50