piiranha-v1-detect-personal-information

Model · Token classification

OpenMed-NER-OncologyDetect-BigMed-278M

OpenMed

Specialized model for Cancer Genetics - Cancer-related genetic entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for cancer genetics - cancer-related genetic 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…

Model · Token classification

xlm-roberta-base-ner-hrl

David Adelani

Hugging Face's logo - multilingual xlm-roberta-base-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a xlm-roberta-base model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers pipeline for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well…

Model · Token classification

OpenMed-NER-PharmaDetect-BigMed-278M

OpenMed

Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - chemical entities from the bc5cdr dataset. 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…

Model · Token classification

intenter

Elda

Korean-first, multilingual (ko · ja · en). 281M parameters · p50 27.8 ms. Elda-Intenter reads an utterance and returns, in one forward pass, four aligned channels: what the speaker is doing (speech act), what they are talking about (entities), and the attribute/predicate structure that binds them. It is not a general-purpose NER model. It is the perception layer of a live conversational system, built for the constraint that shapes everything else in that setting: latency budgets measured in tens of milliseconds. All figures below are on sets excluded from training by construction. KLUE-NER (dev, Korean · 600 sentences / 1,687 gold entities), entity-level F1: Korean particles attach to the…

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.…