Joint entity + relation extraction for Vietnamese. The best-calibrated model in the goby family: higher precision at the same recall than its siblings. On clean text at each model own best threshold it ties goby-4 rather than beating it (see Measurement correction); on degraded text it is the worst of the family. Pick it for well-formed prose where you care about precision. Fine-tuned from knowledgator/gliner-relex-large-v1.0. Released by Belumind. Apache-2.0. Try it in the browser - no install, runs on ZeroGPU. goby-9 is not robust to degraded text - see Known limitations. That is not a small caveat, read it before choosing. Same training data as goby-4. The only difference is the loss…
Open weights
apache-2.0
467M parameters
gliner
Open weights
apache-2.0
467M parameters
gliner
Model · Token classification
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…
Open weights
apache-2.0
434M parameters
512 tokens
transformers
Model · Token classification
OpenMed
Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease 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 applications.…
Open weights
apache-2.0
434M parameters
512 tokens
transformers
Model · Token classification
OpenMed
Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - identifies chemical compounds and substances in biomedical literature. 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…
Open weights
apache-2.0
396M parameters
8,192 tokens
transformers
Model · Token classification
OpenMed
Specialized model for Gene/Protein Entity Recognition - Gene and protein mentions This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene/protein entity recognition - gene and protein mentions. 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…
Open weights
apache-2.0
396M parameters
8,192 tokens
transformers