# Cross-lingual Transfer Learning

A multilingual text classification model based on the xlm-roberta-large architecture, specifically designed for minor topic coding in the Comparative Agendas Project.

A multilingual text classification model based on the xlm-roberta-large architecture, specifically designed for minor topic coding in the Comparative Agenda Project

A multilingual text classification model fine-tuned based on xlm-roberta-large, used for minor topic code classification in comparative agenda projects

A Korean vocabulary expansion version based on microsoft/phi-2, specifically fine-tuned on Korean web-crawled datasets to enhance Korean language understanding.

A base model fine-tuned on the Comparative Agendas Project's major topic code annotated texts for multilingual policy topic classification, built upon xlm-roberta-large.

MathOctopus is a multilingual mathematical reasoning large language model based on the LLaMA 2 architecture, supporting 10 languages and specializing in solving mathematical problems.

This is a Telugu BERT model trained on NLI datasets for sentence similarity calculation.

A multilingual named entity recognition model based on the RoBERTa architecture, supporting entity recognition tasks in 20 languages.

A tag classification model fine-tuned on the xtreme dataset based on xlm-roberta-base

A named entity recognition model fine-tuned on a toy dataset based on the xlm-roberta-large-ner-hrl model

A fine-tuned version of the XLM-RoBERTa-base model on German and French datasets, primarily used for sequence labeling tasks.

German token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base

IndicNER is a model specifically trained for recognizing named entities in sentences of 11 Indian languages, fine-tuned based on the bert-base-multilingual-uncased model.

AfroXLMR-large is a multilingual model created by performing masked language modeling adaptation training on the XLM-R-large model for 17 African languages.

AfroXLMR-base is a multilingual model created by adapting XLM-R-base through masked language modeling on 17 African languages and 3 high-resource languages.

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

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

This model is a fine-tuned version of xlm-roberta-base on English CoNLL-formatted OntoNotes v5.0 semantic role labeling data, specifically designed for English semantic role labeling tasks.

A BERT model based on ClinicalBERT, additionally pre-trained for Bulgarian medical and clinical texts

A multilingual Q&A model based on XLM-Roberta pre-training, supporting English, Tamil, and Hindi, optimized for Indian language Q&A scenarios

IndicBART is a multilingual sequence-to-sequence pre-trained model focused on Indian languages and English, supporting 11 Indian languages, built on the mBART architecture.

This is a named entity recognition model based on the xlm-roberta-base-finetuned-wolof pre-trained model, fine-tuned on the Swahili portion of the MasakhaNER dataset.

A Persian sentence embedding model based on ParsBERT for generating high-quality sentence vector representations

This is a token classification model (specifically for named entity recognition), which was fine-tuned on the Amharic language and then further fine-tuned on the Swahili portion of the MasakhaNER dataset.

This is a pretrained model based on the XLM-RoBERTa architecture, trained with masked language modeling objectives and fine-tuned on English and Russian question answering datasets.

This is a pretrained model specifically for the Khmer language, developed by Tsinghua University team, aiming to support natural language processing tasks in Khmer.

This is a distilled version of distilbert-base-multilingual-cased, specifically optimized for English, Greek, and Russian, maintaining the original model's accuracy.

The XLM model is a cross-lingual language model pre-trained on Wikipedia texts in 100 languages using masked language modeling objectives.