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A DNA annotation model from the Carbon family.

iona-denoise-50m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 50m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-400m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 400m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-200m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 200m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-100m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 100m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

A 350M encoder that finds nine types of personally identifiable information across 17 languages and returns exact character spans for review and redaction.

Healthcare Brain Procedure Surgery NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of surgical procedures, diagnostic tests, interventions, and procedural details from unstructured clinical text.

Healthcare Brain Vitals NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of vital signs, body measurements, and physiological parameters from clinical text.

Healthcare Brain Laboratory NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of laboratory test results, values, units, reference ranges, and abnormality flags from unstructured clinical text.

Healthcare Brain Diagnosis ICD NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of diagnoses, conditions, and support for ICD-10/SNOMED code mapping from unstructured clinical text.

Healthcare Brain Medication NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of medication names, dosages, routes, frequencies, and administration details from unstructured clinical text.

Healthcare Brain Clinical Findings NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of clinical findings, diseases, conditions, anatomical locations, and clinical modifiers from unstructured clinical text.

Healthcare Brain NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated detection and de-identification of Protected Health Information (PHI) and Personally Identifiable Information (PII) in unstructured clinical text.

CliniGuard Laboratory NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of laboratory test results, values, units, reference ranges, and abnormality flags from unstructured clinical text.

CliniGuard Diagnosis ICD NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of diagnoses, conditions, and support for ICD-10/SNOMED code mapping from unstructured clinical text.

CliniGuard Medication NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of medication names, dosages, routes, frequencies, and administration details from unstructured clinical text.

CliniGuard Clinical Findings NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of clinical findings, diseases, conditions, anatomical locations, and clinical modifiers from unstructured clinical text.

CliniGuard Vitals NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of vital signs, body measurements, and physiological parameters from clinical text.

CliniGuard NER is a clinical Named Entity Recognition model developed by Genzeon Platforms for automated detection and de-identification of Protected Health Information (PHI) and Personally Identifiable Information (PII) in clinical text.

PII Detection Model | 44M Parameters | Open Source

PII Detection Model | 434M Parameters | Open Source

Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature

Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature

Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset

View label scheme (20 labels for 1 components)

This model is a high-performance Named Entity Recognition (NER) model designed specifically for medical text. It identifies entities such as diseases, symptoms, procedures, medications, and healthcare providers with high precision and recall, making it ideal for clinical and healthcare applications.

This model is a fine-tuned version of DeBERTa on the PubMED Dataset.

This model may be overfit to some extent (see below). Try running this notebook on the datasets linked to in the notebook. See if you can figure out why the metrics differ so much on the datasets. Is it due to something like sequence similarity in the train/test split?

项目地址：https://github.com/iioSnail/chinesemedicalner

# Model Description This model is a fine-tuned version of BioBERT on the NCBI disease dataset for named entity recognition (NER) of diseases. It can be used to extract disease mentions from unstructured text in the medical and biological domains.