gliner-multitask-large-v0.5
knowledgator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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Explore AI models 2428
gliner-pii-base-v1.0
knowledgatorA production-grade open-source model for privacy-focused PII, PHI, and PCI detection with zero-shot entity recognition capabilities. This model was developed in collaboration between Wordcab and Knowledgator. For enterprise-ready, specialized PII/PHI/PCI models, contact us at info@wordcab.com. GLiNER (Generalist and Lightweight Named Entity Recognition) is a bidirectional transformer model that can identify **any entity type** without predefined categories. Unlike traditional NER models that are limited to specific entity classes, GLiNER allows you to specify exactly what entities you want to extract at runtime. Instead of predicting from a fixed set of entity classes, GLiNER takes both text and a list of desired entity types as input, then identifies spans that match those categories:
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View details ↗gliner-pii-edge-v1.0
knowledgator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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View details ↗gliner-pii-large-v1.0
knowledgatorA production-grade open-source model for privacy-focused PII, PHI, and PCI detection with zero-shot entity recognition capabilities. This model was developed in collaboration between Wordcab and Knowledgator. For enterprise-ready, specialized PII/PHI/PCI models, contact us at info@wordcab.com. GLiNER (Generalist and Lightweight Named Entity Recognition) is a bidirectional transformer model that can identify **any entity type** without predefined categories. Unlike traditional NER models that are limited to specific entity classes, GLiNER allows you to specify exactly what entities you want to extract at runtime. - **Zero-shot recognition**: Extract any entity type without retraining - **Privacy-first**: Process sensitive data locally without API calls - **Lightweight**: Much faster than large language models for NER tasks - **Production-ready**: Quantization-aware training with FP16 and UINT8 ONNX models - **Comprehensive**: 60+ predefined PII categories with custom entity support
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View details ↗gliner-pii-small-v1.0
knowledgator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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View details ↗gliner-relex-large-v0.5
knowledgator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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View details ↗gliner-relex-large-v1.0
knowledgator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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View details ↗gliner-x-base
knowledgator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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View details ↗gliner-x-large
knowledgator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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View details ↗gliner-x-small
knowledgator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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View details ↗opir-multitask-large-v1.0
knowledgator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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View details ↗opir-multitask-multilang-v1.0
knowledgator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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View details ↗Text-Moderation
KoalaAIThis model is a text classification model based on Deberta-v3 that predicts whether a text contains text that could be considered offensive. It is split up in the following labels: It's important to remember that this model was only trained on English texts, and may not perform well on non-English inputs. This is a model that deals with sensitive and potentially harmful language. Users should consider the ethical implications and potential risks of using or deploying this model in their applications or contexts. Some of the ethical issues that may arise are:
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View details ↗bert-japanese-finetuned-sentiment
koheiduck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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View details ↗bert-base-japanese-upos
KoichiYasuoka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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View details ↗bert-base-thai-upos
KoichiYasuokaThis is a BERT model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-th-cased. Every word is tagged by UPOS (Universal Part-Of-Speech).
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View details ↗roberta-base-english-upos
KoichiYasuoka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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View details ↗roberta-base-japanese-luw-upos
KoichiYasuoka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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View details ↗roberta-base-thai-spm-upos
KoichiYasuoka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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View details ↗roberta-classical-chinese-base-ud-goeswith
KoichiYasuoka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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View details ↗roberta-large-english-upos
KoichiYasuoka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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View details ↗redacto-eu-pii-ner-q4f16
koncsik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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View details ↗sbert_punc_case_ru
kontur-aiSbertPuncCase - модель восстановления пунктуации и регистра для русского языка. Модель способна расставлять точки, запятые и знаки вопроса; определять регистр - слово в нижнем регистре, слово с первой буквой в верхнем регистре, слово в верхнем регистре. Модель разработана для восстановления текста после распознавания речи, поэтому работает со строками в нижнем регистре. В основу модели легла sbert_large_nlu_ru. В качестве обучающих данных использованы текстовые расшифровки интервью. 1. Текст переводится в нижний регистр и разбивается на слова. 2. Слова разделяются на токены. 3. Модель (по аналогии с задачей NER) предсказывает класс для каждого токена. Классификация на 12 классов: 3+1 знака препинания * 3 варианта регистра. 4. Функция декодировки восстанавливает текст соответственно предсказанным классам.
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View details ↗en_core_med7_lg
kormilitzin`en_core_med7_lg` is a non-transformer, large-vector spaCy pipeline for medication-related named entity recognition in English clinical text. Unlike `en_core_med7_trf`, this model does not use a transformer backend and does not require `spacy-transformers`, `transformers`, `tokenizers`, or `torch` at runtime. Kormilitzin, A., Vaci, N., Liu, Q., & Nevado-Holgado, A. (2021). Med7: A transferable clinical natural language processing model for electronic health records. *Artificial Intelligence in Medicine*, 118, 102086. https://doi.org/10.1016/j.artmed.2021.102086
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