# XLM-RoBERTa fine-tuning

XLM-RoBERTa-large fine-tuned Uzbek named entity recognition model supporting 21 entity types

This model is a demonstration classifier designed to identify problematic content (wrong language, garbled text) in Danish and Swedish web pages.

A Khmer fill-mask model built based on the FacebookAI/xlm-roberta-base pre-trained model, fine-tuned with 26,000+ Khmer sentences.

A named entity recognition model fine-tuned based on the XLM-RoBERTa base model, supporting multiple languages and capable of identifying three types of entities: locations, organizations, and persons.

A fill-mask model fine-tuned based on xlm-roberta-base for predicting masked text segments

Estonian feature extraction model fine-tuned based on XLM-RoBERTa base model

A cross-lingual model fine-tuned on German and French datasets based on XLM-RoBERTa-base, primarily used for named entity recognition tasks.

A token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base, supporting Italian text processing

A tagging classification model fine-tuned on the xtreme_en dataset based on xlm-roberta-base, supporting multilingual text processing.

A Ukrainian Q&A model fine-tuned based on xlm-roberta-base-uk, optimized for Ukrainian question-answering tasks

XLM-RoBERTa-based language detection model supporting text classification in 20 languages

This is an XLM-RoBERTa model fine-tuned for named entity recognition tasks, suitable for entity recognition in multilingual texts.

This model is an Icelandic POS tagging model based on the XLM-RoBERTa architecture, fine-tuned on the Universal Dependencies dataset v2.8.

A named entity recognition model fine-tuned based on XLM-RoBERTa large, supporting 10 high-resource languages, capable of identifying three types of entities: location, organization, and person.

A multilingual named entity recognition model based on XLM-RoBERTa, supporting 10 high-resource languages, capable of identifying three types of entities: location, organization, and person

This is a Hungarian sentence-level sentiment analysis model fine-tuned based on the XLM-RoBERTa pre-trained model, supporting 5 sentiment classifications.