Sold by: OpenMedÂ
Deployed on AWS
Open-source NER model for gene entities in biomedical and clinical text. Trained on BC2GM and optimized for state-of-the-art precision, it enables reliable extraction with fast, easy deployment via Hugging Face Transformers.
Overview
Open-Source Gene 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 gene entities from biomedical and clinical documents. Engineered on the curated BC2GM dataset, this model surpasses licensed alternatives with industry-leading precision. Why Choose OpenMed Gene 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, outperforming commercial solutions. Clinical & Biomedical Excellence: Expertly validated on clinical benchmarks for reliability in genomics, healthcare analytics, and gene studies. Easy & Fast Integration: Seamlessly integrates into the Hugging Face Transformers ecosystem for effortless deployment. Ideal for Biomedical Applications Including: Gene interaction detection Gene extraction from patient records Genetic variant monitoring Literature mining for gene research Biomedical knowledge graph construction Genomics informatics and gene research Built on the BC2GM Dataset: This specialized dataset contains comprehensive annotations for gene names and mentions, making it ideal for genomics informatics, gene research, and advanced biomedical text mining. Entity Types Supported: B-GENE/PROTEIN OpenMed-GENE/PROTEIN 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 BC2GM 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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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
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
fixing the ARN
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": "Expression of TP53 and EGFR is elevated in tumor samples. MYC amplification suspected."}
{"inputs": ["Expression of TP53 and EGFR is elevated in tumor samples. MYC amplification suspected.", "Downregulation of BRCA1 and upregulation of KRAS observed in biopsy results."]}
Support
Vendor support
For any questions or support, please join the discussions on OpenMed:
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