bge-small-ner-early-stopping

A 35M-parameter token tagger for personal data in Chinese and mixed Chinese–English text. It is a fine-tune of cjhwong/seatbelt-pii-tagger with the same architecture, tokenizer and 18 types / 73 BIOES labels. - real Chinese contexts (FineWeb-2) with inserted synthetic private names and addresses; - self-distillation replay, which aims to reduce forgetting of the base model's other types and its English behaviour. - It misses fewer Chinese person names. On test sets never used during development, person recall rises by about 2 to 10 points; precision moves between −3.5 and +3.5 points. - +0.6 to +2.1 on two freshly generated test sets; - +4.2 to +6.7 on an MSRA NER train sample (never used…

Model · Token classification

bert-small-pii-detection

Gravitee.io

Token-classification model for PII detection, fine-tuned from prajjwal1/bert-small on Detect personally identifiable information (PII) spans in english text. Suitable for privacy filtering, redaction pipelines, and data-leak prevention particularly on structured data (JSON, HTML, XML, SQL, Document) English-focused; other languages will degrade External-corpus evaluation (English only), seqeval. Last run: 2026-05-21. Data citation are present in the dataset card used for this model. If you use the model, please consider citing the papers

This model is a fine-tuned version of monologg/koelectra-small-v3-discriminator on an unknown dataset. It achieves the following results on the evaluation set: 태깅 시스템: BIO 시스템 한국정보통신기술협회(TTA) 대분류 기준을 따르는 15 가지의 태그셋 You can use this model with Transformers pipeline for NER. 개체명 인식(NER) 모델 학습 데이터 셋 - https://corpus.korean.go.kr/request/reausetMain.do The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 64 - evalbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupsteps: 15151 - numepochs: 20 - mixedprecisiontraining: Native AMP - Transformers 4.27.4 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Model · Token classification

OpenMed-NER-BloodCancerDetect-TinyMed-65M

OpenMed

Specialized model for Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for clinical entity recognition - clinical entities related to chronic lymphocytic leukemia. 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…

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! distilbert-NER is the fine-tuned version of DistilBERT, which is a distilled variant of the BERT model. DistilBERT has fewer parameters than BERT, making it smaller, faster, and more efficient. distilbert-NER is specifically fine-tuned for the task of Named Entity Recognition (NER). This model accurately identifies the same four types of entities as its BERT counterparts: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC). Although it is a more compact model…

Model · Token classification

biomedical-ner-all

D 4 Data Community

An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18 The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at