Sold by: OpenMedÂ
Deployed on AWS
Open-source NER model for cancer genetics entities in biomedical and clinical text. Trained on BioNLP2013-CG and optimized for state-of-the-art precision, it enables reliable extraction with fast, easy deployment via Hugging Face Transformers.
Overview
Open-Source Cancer Genetics NER Model: State-of-the-Art Biomedical Entity Recognition Discover a powerful, open-source Named Entity Recognition (NER) model specially fine-tuned for accurate identification and extraction of cancer genetics entities from biomedical and clinical documents. Engineered on the curated BioNLP2013-CG dataset, this model surpasses licensed alternatives with industry-leading precision. Why Choose OpenMed Cancer Genetics NER Model? Open-Source & Free Forever: No licensing fees fully accessible to empower your biomedical research. State-of-the-Art Accuracy: Achieve unmatched precision in extracting cancer-related genes and proteins, outperforming commercial solutions. Clinical & Biomedical Excellence: Expertly validated on clinical benchmarks for reliability in oncology, healthcare analytics, and cancer research. Easy & Fast Integration: Seamlessly integrates into the Hugging Face Transformers ecosystem for effortless deployment. Ideal for Biomedical Applications Including: Cancer gene detection Oncology protein extraction from patient records Cancer biomarker monitoring Literature mining for cancer research Biomedical knowledge graph construction Oncology informatics and cancer research Built on the BioNLP2013-CG Dataset: This specialized dataset contains comprehensive annotations for cancer-related genes, proteins, and cancer entities, making it ideal for oncology informatics, cancer research, and advanced biomedical text mining. Entity Types Supported: B-Gene_or_gene_product OpenMed-Gene_or_gene_product B-Cancer OpenMed-Cancer B-Cell OpenMed-Cell B-Tissue OpenMed-Tissue B-Organ OpenMed-Organ B-Organism OpenMed-Organism B-Simple_chemical OpenMed-Simple_chemical B-Amino_acid OpenMed-Amino_acid B-Cellular_component OpenMed-Cellular_component B-Anatomical_system OpenMed-Anatomical_system B-Developing_anatomical_structure OpenMed-Developing_anatomical_structure B-Multi-tissue_structure OpenMed-Multi-tissue_structure B-Organism_subdivision OpenMed-Organism_subdivision B-Organism_substance OpenMed-Organism_substance B-Pathological_formation OpenMed-Pathological_formation Experience industry-leading biomedical NER performance open-source and completely free.
Highlights
- Open-Source and Free Forever: Eliminate licensing costs while accessing state-of-the-art biomedical entity recognition.
- Clinical-Grade Accuracy: Superior precision validated on the BioNLP2013-CG dataset, ideal for oncology research and mutation tracking.
- Easy Integration: Fully compatible with Hugging Face Transformers for fast and effortless deployment.
Details
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Latest version
Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.00 |
ml.t2.medium Inference (Real-Time) | Model inference on the ml.t2.medium instance type, real-time mode | $0.00 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.00 |
ml.c5.large Inference (Real-Time) | Model inference on the ml.c5.large instance type, real-time mode | $0.00 |
ml.c5.xlarge Inference (Real-Time) | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.00 |
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Delivery details
Amazon SageMaker model
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Version release notes
Initial release of OpenMed NER models for comprehensive biomedical entity recognition. These models deliver high‑precision token classification across clinical and research text, covering organisms and species, chemicals, diseases and phenotypes, genes and proteins, genetic variants/genomics, anatomy, oncology (including CLL), and related biomedical concepts. Designed for enterprise‑grade accuracy, optimized performance on medical text, and production‑ready reliability for healthcare and life sciences applications.
Additional details
Inputs
- Summary
This model accepts clinical text, research papers, and biomedical documents as input. The input should be provided as JSON with an "inputs" field containing the text to analyze. The model processes natural language text and identifies medical entities including organisms, species, diseases, and other biomedical terms. Input text can range from short clinical notes to longer research documents, with optimal performance on medical and scientific content.
- Input MIME type
- application/json
{"inputs": "NGS panel shows EGFR L858R and T790M mutations. Additional copy number gain in ERBB2 detected."}
{"inputs": ["NGS panel shows EGFR L858R and T790M mutations. Additional copy number gain in ERBB2 detected.", "Report indicates ALK fusion and MET amplification. KRAS G12C variant observed in metastatic lesion."]}
Support
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For any questions or support, please join the discussions on OpenMed:
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