# Named entity recognition

A RoBERTa model specifically designed for Amharic, which solves the problem of insufficient performance in Amharic NLP tasks through pre-training from scratch.

FRED-T5-large-instruct-v0.1 is a Russian text auto-editing and question-answering model based on PyTorch and Transformers, primarily used for various Russian text processing tasks.

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

Swedish BERT base model released by the National Library of Sweden/KBLab, trained on multi-source texts

BiodivBERT is a domain-specific model based on BERT, specifically designed for biodiversity literature.

Provides Traditional Chinese transformers models and NLP tools including word segmentation, POS tagging, and named entity recognition.

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

ViHealthBERT is a pre-trained language model for Vietnamese health text mining, providing strong baseline performance in the healthcare domain

This model is a Ukrainian version of roberta-base migrated via the WECHSEL method, demonstrating excellent performance across multiple Ukrainian NLP tasks.

Latvian pre-trained language model based on BERT architecture, suitable for various natural language understanding tasks

BERT-base model pretrained on a decade of StackOverflow archive data, specializing in code and named entity recognition tasks.

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

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

A lightweight ALBERT model developed by Academia Sinica's CKIP team, supporting Traditional Chinese NLP tasks

A Traditional Chinese natural language processing model developed by the Academia Sinica CKIP team, based on the ALBERT architecture, supporting tasks such as word segmentation and part-of-speech tagging.

CZERT is a language representation model specifically trained for Czech, outperforming multilingual BERT models on various Czech NLP tasks

This model is used to automatically add punctuation marks to unpunctuated classical Chinese texts, supporting over twenty types of punctuation.

This is a Traditional Chinese ALBERT model developed by Academia Sinica's CKIP team, suitable for various natural language processing tasks.

Traditional Chinese BERT model developed by Academia Sinica CKIP Lab, supporting natural language processing tasks

Stanza is a precise and efficient toolkit for syntactic analysis of multiple human languages. From raw text to syntactic parsing and named entity recognition, Stanza provides state-of-the-art natural language processing models for your chosen language.

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

Stanza is a precise and efficient toolkit for syntactic analysis of multiple human languages. From raw text to syntactic parsing and named entity recognition, Stanza brings state-of-the-art natural language processing models to your chosen language.

A Traditional Chinese Transformer model developed by the Lexical Knowledge Base Group of Academia Sinica, including architectures such as ALBERT, BERT, GPT2 and natural language processing tools

AfriBERTa large is a pre-trained multilingual model with approximately 126 million parameters, supporting 11 African languages, suitable for tasks such as text classification and named entity recognition.

Provides Traditional Chinese transformers models and natural language processing tools

Traditional Chinese natural language processing model developed by Academia Sinica's CKIP team, supporting tasks like word segmentation and part-of-speech tagging

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

This is a BERT model optimized for the Igbo language, obtained by fine-tuning on top of the multilingual BERT, and it performs better than the base multilingual model in Igbo text processing tasks.

UmBERTo is an Italian language model based on the Roberta architecture, trained using SentencePiece and whole word masking techniques, suitable for various natural language processing tasks.

This model is a BERT model trained on over 20GB of Vietnamese news datasets, suitable for tasks such as sentiment analysis, and performs excellently on the AIViVN comment dataset.