Sold by: OpenMed
Deployed on AWS
Open-source NER model for protein entities in biomedical and clinical text. Trained on FSU and optimized for state-of-the-art precision, it enables reliable extraction with fast, easy deployment via Hugging Face Transformers.
Overview
Open-Source Protein 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 protein entities from biomedical and clinical documents. Engineered on the curated FSU dataset, this model surpasses licensed alternatives with industry-leading precision. Why Choose OpenMed Protein 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 protein names, complexes, and families, outperforming commercial solutions. Clinical & Biomedical Excellence: Expertly validated on clinical benchmarks for reliability in protein biology, healthcare analytics, and systems biology studies. Easy & Fast Integration: Seamlessly integrates into the Hugging Face Transformers ecosystem for effortless deployment. Ideal for Biomedical Applications Including: Protein interaction detection Protein extraction from patient records Protein complex monitoring Literature mining for protein research Biomedical knowledge graph construction Protein informatics and systems biology research Built on the FSU Dataset: This specialized dataset contains comprehensive annotations for proteins, protein complexes, and protein families, making it ideal for protein informatics, systems biology research, and advanced biomedical text mining. Entity Types Supported: B-protein OpenMed-protein B-protein_complex OpenMed-protein_complex B-protein_enum OpenMed-protein_enum B-protein_familiy_or_group OpenMed-protein_familiy_or_group B-protein_variant OpenMed-protein_variant 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 FSU dataset, ideal for proteomics research and literature mining.
- Easy Integration: Fully compatible with Hugging Face Transformers for fast and effortless deployment.
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Dimensions summary
This model is free to use; you pay only for the AWS compute you run. Pricing splits into two inference modes. Batch mode processes datasets in bulk and runs on general-purpose ml.m5 sizes (large, 2xlarge, 4xlarge) plus a compute-optimized ml.c5.2xlarge. Real-time mode serves live requests and runs on ml.t2.medium, ml.m5 (large, xlarge), and ml.c5 (large, xlarge). You pick a mode, then an instance size. Larger instances add CPU and memory, so hourly cost rises with size. Billing is per host hour for each running instance.
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Each instance size gives a fixed amount of CPU and memory. The ml.m5.4xlarge holds more vCPUs and RAM than the ml.m5.large. Larger sizes process more data at once. You pick the size that fits your workload, and pay per host hour for whichever size runs.
Software charges apply per host hour while an instance runs. A fully stopped instance does not accrue software charges. Real-time instances stay running to serve live requests, so they meter continuously. Batch instances run only while a job processes, then stop.
Both meter per host hour on the instance you select. Batch mode runs only during a bulk job, so hours stop when the job finishes. Real-time mode serves live requests, so the instance runs continuously and meters for the whole time it stays up.
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Amazon SageMaker model
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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": "Increased levels of C-reactive protein and TNF-alpha detected. Elevated EGFR protein expression in tumor tissue."}
{"inputs": ["Increased levels of C-reactive protein and TNF-alpha detected. Elevated EGFR protein expression in tumor tissue.", "Serum hemoglobin is low while albumin is within normal range. Overexpression of HER2 protein noted."]}
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