Google
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Input tokens/M
Output tokens/M
Context Length
Delicalib
This is a Russian patent named entity recognition model based on the spaCy framework, focusing on identifying specific entity types in patent texts.
nestauk
en_skillner is a named entity recognition model specifically designed to extract skills, experience, and benefits from recruitment advertisements. This model is built on the spaCy framework and can accurately identify three key types of entities in recruitment texts. Since May 2025, Nesta has stopped further development and maintenance of this model.
Priyanka-Balivada
A spaCy-based named entity recognition model specifically designed for extracting key information from resumes.
LPDoctor
A spaCy NER model optimized for occupation, facility, and experience entities, suitable for text analysis in the job applicant domain
latincy
A Latin processing model based on spaCy, supporting multiple natural language processing tasks.
PlanTL-GOB-ES
A multilingual (Catalan and Spanish) anonymization model based on Spacy, used to identify and anonymize sensitive data.
turkish-nlp-suite
Medium-sized spaCy pipeline optimized for Turkish, including tokenization, part-of-speech tagging, morphological analysis, dependency parsing, and named entity recognition
opennyaiorg
This is an Indian legal named entity recognition model trained based on the spaCy framework. It is specifically designed to identify various legal entities in Indian legal judgment texts and achieved an F1 score of 91.076 in testing. It supports the recognition of 14 types of legal entities.
spacy
Large Croatian language processing model provided by spaCy, suitable for various NLP tasks
A CPU-optimized Finnish language processing pipeline provided by spaCy, featuring comprehensive NLP capabilities including POS tagging, dependency parsing, and named entity recognition
CPU-optimized Finnish language processing pipeline with NLP features including token classification and dependency parsing
CPU-optimized Korean processing pipeline with complete NLP capabilities including tokenization, POS tagging, dependency parsing, named entity recognition, etc.
CPU-optimized Korean processing pipeline with complete NLP functions including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, etc.
Korean processing pipeline optimized for CPU, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, etc.
A Swedish natural language processing pipeline optimized for CPU, including complete NLP components such as part-of-speech tagging and named entity recognition
Swedish small natural language processing model provided by spaCy, optimized for CPU, including complete NLP pipeline such as tokenization, part-of-speech tagging, and dependency parsing
browndw
A spaCy pipeline for English POS and rhetorical annotation, supporting named entity recognition and part-of-speech tagging tasks.
CPU-optimized Spanish processing pipeline, including tokenization, POS tagging, dependency parsing, named entity recognition, etc.
Large Russian NLP model provided by spaCy, optimized for CPU, featuring a complete NLP processing pipeline
CPU-optimized Dutch processing pipeline containing various natural language processing components.
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