Sold by: NYSgpt
Deployed on AWS
Biomedical literature reranker that beats the US National Library of Medicine's own MedCPT reranker on concept search.
Overview
A cross-encoder reranker for biomedical literature, fine-tuned from Alibaba-NLP/gte-reranker-modernbert-base on bioRxiv and medRxiv preprints. Give it a query and candidate papers; it returns them ranked by relevance.
Beats the National Library of Medicine's reranker
Handed the exact candidate pool of the NLM's own MedCPT cross-encoder, it ranks better on biomedical concept search: Recall@10 0.544 vs 0.525 (p = 0.013) and nDCG@10 +8%, a paired win significant in both query families, measured on queries searched against all 433,449 bioRxiv and medRxiv preprints. Combined with the NYSgpt Biomedical Encoder, the two-stage system also edges the NLM's complete retriever-plus-reranker stack (0.549 vs 0.525 Recall@10).
Input and output
Input: JSON {"query": "text", "documents": ["text", ...]}. Output: relevance scores and a ranking, highest first.
Runs entirely in your account
CPU inference on ml.m5 and ml.c5 instances. Model card: https://huggingface.co/NYSgpt/biomed-reranker
Highlights
- Beats the NLM's MedCPT cross-encoder on its own candidates: Recall@10 0.544 vs 0.525
- +8% nDCG@10, a statistically significant paired win (p = 0.013)
- Pairs with the NYSgpt Biomedical Encoder for a complete search stack
Details
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Pricing
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $3.75 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $3.75 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $3.75 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $3.75 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $3.75 |
Vendor refund policy
Software fees are billed hourly while your endpoint or batch job runs; stop the endpoint to stop charges. Contact Brendan@nysgpt.com about billing issues within 30 days.
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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.
Additional details
Inputs
- Summary
A query and a list of candidate documents: JSON {"query": "text", "documents": ["text", ...], "top_k": 10}, or {"pairs": [["query", "document"], ...]}.
- Limitations for input type
- Queries and documents are trimmed to the lengths used in training.
- Input MIME type
- application/json
{"query": "CRISPR screens in T cells", "documents": ["A CRISPR knockout screen identifies regulators of T cell exhaustion", "Wastewater surveillance of SARS-CoV-2 variants"]}
https://github.com/nyd-user-1/nysgpt-rerankers-sagemaker/blob/main/data/sample-input.json
Support
Vendor support
Support by email from the publisher (Brendan@nysgpt.com ); typical response within one business day. Usage notebook:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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