OpenMed-NER-DiseaseDetect-BioMed-335M

Model · Token classification

OpenMed-NER-GenomicDetect-PubMed-335M

OpenMed

Specialized model for Gene Entity Recognition - Gene-related entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene entity recognition - gene-related entities. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can identify and classify the…

If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks! bert-large-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a bert-large-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset. If you'd like to use a smaller BERT model fine-tuned…

Model · Token classification

deid_roberta_i2b2

One Brave Idea

A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) model fine-tuned for de-identification of medical notes. A token can either be classified as non-PHI or as one of the 11 PHI types. Token predictions are aggregated to spans by making use of BILOU tagging. The PHI labels that were used for training and other details can be found here: Annotation Guidelines More details on how to use this model, the format of data and other useful information is present in the GitHub repo: Robust DeID. A demo on how the model works (using model predictions to de-identify a medical note) is on this space: Medical-Note-Deidentification. Steps on how this model can be used to run a forward…

Model · Token classification

gliner2.5-multi-v1

Fastino

GLiNER2.5 Multi is the multilingual boundary checkpoint. It is built on mDeBERTa-v3-base and is the default choice when you need entities, classification, records, and relations in one model across languages. Load it with AutoExtractor: the checkpoint's architecture field selects BoundaryExtractor automatically. Fine-tune via Fastino. Join discussions on Reddit. This card is for fastino/gliner2.5-multi-v1. All three checkpoints share the same public API. Python 3.10 or newer is required. The [local] extra pulls in PyTorch so you can load Hub checkpoints. Always use AutoExtractor for GLiNER2.5. GLiNER2.frompretrained(...) is the legacy span loader and will not dispatch this checkpoint.…

Model · Token classification

piiranha-v1-detect-personal-information

The Internet Integrity Initiative

william (at) integrinet [dot] org Piiranha (cc-by-nc-nd-4.0 license) is trained to detect 17 types of Personally Identifiable Information (PII) across six languages. It successfully catches 98.27% of PII tokens, with an overall classification accuracy of 99.44%. Piiranha is especially accurate at detecting passwords, emails (100%), phone numbers, and usernames. Performance on PII vs. Non PII classification task: Piiranha was trained on H100 GPUs generously sponsored by the Akash Network Piiranha is a fine-tuned version of microsoft/mdeberta-v3-base. The context length is 256 Deberta tokens. If your text is longer than that, just split it up. Supported PII types: Account Number, Building…