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rampart

nationaldesignstudio

`rampart` is a 14.7 MB ONNX token-classification model that detects personally identifiable information (PII) in text before it leaves the user's device. It is the on-device half of **Rampart**, a defense-in-depth client-side redaction system released by National Design Studio. The shipped artifact runs alongside a deterministic recognizer layer that handles structured identifiers; together they form the complete system. This card documents the released artifact only. Alternative configurations explored during model selection (an ELECTRA-small base, the prefilter-off training variant, leaner data mixes, and smaller corpus slices) are discussed in the project whitepaper for context but are not published. The model is designed for **client-side redaction of user-typed text in AI assistants and intake flows** — replacing identifying values with stable placeholders before any data is transmitted to a model provider, a server, or a logging system.

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ESG-BERT

nbroad

The applications of ESG-BERT can be expanded way beyond just text classification. It can be fine-tuned to perform various other downstream NLP tasks in the domain of Sustainable Investing. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.

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This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine. This tool shows the results of research conducted in the Computational Biology Branch, NCBI/NLM. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health behavior solely on the basis of information produced on this website. NIH does not independently verify the validity or utility of the information produced by this tool. If you have questions about the information produced on this website, please see a health care professional. More information about NCBI's disclaimer policy is available.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text analysis
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This model is an export of this FastText Language Identification model for `staticvectors`. `staticvectors` enables running inference in Python with NumPy. This helps it maintain solid runtime performance. Language detection is an important task and identification with n-gram models is an efficient and highly accurate way to do it. _This model is a quantized version of the base language id model. It's using 2x256 Product Quantization like the original quantized model from FastText. This shrinks this model down to 4MB with only a minor hit on accuracy._

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This is a PubMedBERT-base model fined-tuned using sentence-transformers. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of PubMed title-abstract pairs along with similar title pairs. PubMedBERT Embeddings produces higher quality embeddings than generalized models for medical literature. Further fine-tuning for a medical subdomain will result in even better performance. This model can be used to build embeddings databases with txtai for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).

Text analysisOpen weights
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ToxiGuardrail

nicholasKluge

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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