Sold by: OpenMed
Deployed on AWS
Open-source NER model for genetics entities in biomedical and clinical text. Trained on GELLUS and optimized for state-of-the-art precision, it enables reliable extraction with fast, easy deployment via Hugging Face Transformers.
Overview
Open-Source 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 genetics entities from biomedical and clinical documents. Engineered on the curated GELLUS dataset, this model surpasses licensed alternatives with industry-leading precision. Why Choose OpenMed 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 gene names and genetic variants, outperforming commercial solutions. Clinical & Biomedical Excellence: Expertly validated on clinical benchmarks for reliability in genetics, healthcare analytics, and genomics studies. Easy & Fast Integration: Seamlessly integrates into the Hugging Face Transformers ecosystem for effortless deployment. Ideal for Biomedical Applications Including: Gene interaction detection Genetic variant extraction from patient records Gene monitoring Literature mining for genetics research Biomedical knowledge graph construction Genetics informatics and genomics research Built on the GELLUS Dataset: This specialized dataset contains comprehensive annotations for genes and genetic variants, making it ideal for genetics informatics, genomics research, and advanced biomedical text mining. Entity Types Supported: B-Cell-line-name OpenMed-Cell-line-name 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 GELLUS dataset, ideal for genomics pipelines and literature mining.
- Easy Integration: Fully compatible with Hugging Face Transformers for fast and effortless deployment.
Details
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Dimensions summary
This model is free to use, so you pay only for the AWS compute you run it on. Pricing splits into two modes. Batch inference runs on four instance types (ml.m5.large, ml.m5.2xlarge, ml.m5.4xlarge, and ml.c5.2xlarge) for processing data in bulk. Real-time inference runs on five instance types (ml.t2.medium, ml.m5.large, ml.m5.xlarge, ml.c5.large, and ml.c5.xlarge) for live requests. You pay per host hour. Larger or more powerful instances cost more per hour, so you pick the size that fits your workload.
Top-of-mind questions for buyers
One host hour is one hour that a running inference instance is active in your account. Charges accrue per hour the instance runs. If you stop the endpoint, software metering stops. Underlying AWS resources like storage may still bill separately while stopped.
Both meter per host hour on the instance you choose. Batch inference processes data in bulk, so you run an instance only while a job runs. Real-time inference keeps an instance running to serve live requests, so hours accrue for as long as the endpoint stays up.
The model software carries no license charge. You pay only for the AWS compute instance hours used to run inference. Your bill depends on which instance type you select and how many hours it runs, in either batch or real-time mode.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
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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.
- Limitations for input type
- Maximum input length: 512 tokens per request. For longer documents, split into smaller chunks. Input should be in English. Best performance achieved with medical, clinical, or biomedical text content.
- Input MIME type
- application/json
{"inputs": "Tumor profiling identified KRAS G12D and BRAF V600E variants. Additional findings include PIK3CA mutations."}
[{"entity_group": "VARIANT", "score": 0.9754, "word": "KRAS G12D", "start": 27, "end": 36}, {"entity_group": "VARIANT", "score": 0.9965, "word": "BRAF V600E", "start": 41, "end": 51}]
Support
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For any questions or support, please join the discussions on OpenMed:
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