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Medroberta.Nl: An Anonymous Dutch Medical Language Model Pre-Trained on Electronic Health Records

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Soft-Prompt Tuning to Predict Lung Cancer Using Primary Care Free-Text Dutch Medical Notes
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Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1
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MIMIC-III, a freely accessible critical care database
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Automated recognition of functioning, activity and participation in COVID-19 from electronic patient records by natural language processing: a proof- of- concept
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Topic evolution before fall incidents in new fallers through natural language processing of general practitioners’ clinical notes
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
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Leveraging Clinical BERT in Multimodal Mortality Prediction Models for COVID-19
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Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
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Membership Inference Attacks Against Machine Learning Models
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Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
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Impact of Tokenization on Language Models: An Analysis for Turkish
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SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation
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SpanBERT: Improving Pre-training by Representing and Predicting Spans
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A Primer in BERTology: What We Know About How BERT Works
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Extracting patient lifestyle characteristics from Dutch clinical text with BERT models
inferredcites →https://doi.org/10.1609/aaai.v34i05.6451openalex-extracted
Neural Machine Translation with Byte-Level Subwords
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.261openalex-extracted
LEGAL-BERT: The Muppets straight out of Law School
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.292openalex-extracted
RobBERT: a Dutch RoBERTa-based Language Model
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.243openalex-extracted
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
inferredcites →https://doi.org/10.18653/v1/d19-1371openalex-extracted
SciBERT: A Pretrained Language Model for Scientific Text
inferredcites →https://doi.org/10.18653/v1/s17-2001openalex-extracted
SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
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Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-extracted
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.4230/lipics.cp.2025.31openalex-extracted
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-extracted
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1904.05342openalex-extracted
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
inferredcites →https://doi.org/10.48550/arxiv.1909.11942openalex-extracted
ALBERT-Base v2 (albert_base_v2.tar.gz): preservation copy
inferredcites →https://doi.org/10.48550/arxiv.1912.09582openalex-extracted
BERTje: A Dutch BERT Model
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Privacy in Deep Learning: A Survey
inferredcites →https://doi.org/10.48550/arxiv.2005.07503openalex-extracted
COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter
inferredcites →https://doi.org/10.48550/arxiv.2112.02998openalex-extracted
A PubMedBERT-based Classifier with Data Augmentation Strategy for Detecting Medication Mentions in Tweets
inferredcites →https://doi.org/10.52202/075280-1608openalex-extracted
Language Model Tokenizers Introduce Unfairness Between Languages
inferredcites →https://doi.org/10.63317/3ooibkrndr77openalex-extracted
Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients
inferredcites →https://doi.org/10.63317/4jkv4oq2gtcfopenalex-extracted
Efficiently and Thoroughly Anonymizing a Transformer Language Model for Dutch Electronic Health Records: a Two-Step Method
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