Sold by: OpenMedÂ
Deployed on AWS
Open-source NER model for chemical entities in biomedical and clinical text. Trained on BC4CHEMD and optimized for state-of-the-art precision, it enables reliable extraction with fast, easy deployment via Hugging Face Transformers.
Overview
Open-Source Chemical 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 chemical entities from biomedical and clinical documents. Engineered on the curated BC4CHEMD dataset, this model surpasses licensed alternatives with industry-leading precision. Why Choose OpenMed Chemical 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 chemical names, outperforming commercial solutions. Clinical & Biomedical Excellence: Expertly validated on clinical benchmarks for reliability in drug discovery, healthcare analytics, and chemical studies. Easy & Fast Integration: Seamlessly integrates into the Hugging Face Transformers ecosystem for effortless deployment. Ideal for Biomedical Applications Including: Drug interaction detection Chemical compound extraction from patient records Adverse event monitoring Literature mining for drug discovery Biomedical knowledge graph construction Chemical informatics and pharmacology research Built on the BC4CHEMD Dataset: This specialized dataset contains comprehensive annotations for chemical compounds and substances, making it ideal for chemical informatics, pharmacology research, and advanced biomedical text mining. Entity Types Supported: B-CHEM OpenMed-CHEM 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 BC4CHEMD dataset, ideal for drug discovery and pharmacovigilance.
- Easy Integration: Fully compatible with Hugging Face Transformers for fast and effortless deployment.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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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
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": "Patient received acetaminophen and ibuprofen for pain management. Exposure to benzene was noted in occupational history."}
{"inputs": ["Patient received acetaminophen and ibuprofen for pain management. Exposure to benzene was noted in occupational history.", "Therapy includes metformin and amlodipine. Elevated levels of caffeine and nicotine detected in samples."]}
Support
Vendor support
For any questions or support, please join the discussions on OpenMed:
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