# Dependency parsing

This is a BERT model pretrained on Vietnamese text for POS tagging and dependency parsing.

Large-scale Turkish natural language processing pipeline, including tokenization, POS tagging, morphological analysis, lemmatization, dependency parsing, and named entity recognition.

Turkish transformer pipeline for TrSpaCy, including transformer, tokenizer, morphological analyzer, lemmatizer, parser, and named entity recognizer components.

Large Croatian language processing model provided by spaCy, suitable for various NLP tasks

RoBERTa-based model for Coptic POS tagging and dependency parsing

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 Korean processing pipeline with complete NLP capabilities including tokenization, POS tagging, dependency parsing, named entity recognition, etc.

This is a RoBERTa model specifically designed for Serbian language, specialized in part-of-speech tagging and dependency parsing.

Transformer pipeline (huBERT) for Hungarian HuSpaCy, containing multiple natural language processing components

BERT model pre-trained on UD_Russian for Russian POS tagging and dependency parsing

RoBERTa model pretrained on Ukrainian UberText corpus, specialized for POS tagging (UPOS) and dependency parsing tasks

A Slovak POS tagging model fine-tuned on Universal Dependencies sk_snk dataset based on distil-slovakbert

CPU-optimized Danish language processing pipeline including tokenization, POS tagging, dependency parsing, named entity recognition, and other complete NLP functionalities

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.

DaCy is a Danish language processing framework featuring state-of-the-art pipelines and analysis capabilities for Danish.

CPU-optimized Spanish processing pipeline, including tokenization, POS tagging, dependency parsing, named entity recognition, etc.

CPU-optimized Italian processing pipeline including tokenization, POS tagging, dependency parsing, named entity recognition, etc.

A CPU-optimized Portuguese processing pipeline, including tokenization, POS tagging, dependency parsing, named entity recognition, and more.

Multilingual POS tagging model based on XLM-RoBERTa, specifically optimized for Slovak

spaCy's CPU-optimized Russian processing pipeline, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and other features

Multilingual POS tagging model based on XLM-RoBERTa, supporting Catalan and other languages

Large-scale Hungarian natural language processing model based on the spaCy framework, supporting multiple NLP tasks.

CPU-optimized German processing pipeline, including tokenization, POS tagging, morphological analysis, dependency parsing, lemmatization, named entity recognition, etc.

spaCy's CPU-optimized Japanese processing pipeline, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and more

DaCy is a Danish language processing framework that provides state-of-the-art pipelines and functionalities for analyzing Danish language pipelines.

CPU-optimized Polish natural language processing model supporting POS tagging, named entity recognition, dependency parsing and other tasks

Large French language processing model provided by spaCy, optimized for CPU, supporting multiple NLP tasks

BERT model pre-trained on Thai Wikipedia text for POS tagging and dependency parsing

English POS tagging and dependency parsing model based on XLM-RoBERTa

BERT model pre-trained on Chinese Wikipedia texts for POS tagging and dependency parsing.

A RoBERTa model pre-trained on Aozora Bunko texts for Japanese POS tagging and dependency parsing.

A RoBERTa model pretrained on Thai Wikipedia text for POS tagging and dependency parsing

BERT model pre-trained on Japanese Wikipedia text for POS tagging and dependency parsing

A RoBERTa model pretrained on Aozora Bunko texts using a character tokenizer, suitable for Japanese text processing tasks.

English POS tagging and dependency parsing model based on RoBERTa-large, trained on the Universal Dependencies dataset.