# BERT Architecture Optimization

BERT-based multilingual detection model supporting text classification for 200 languages

This is a LayoutLM model pre-trained on Japanese text, primarily used for token classification tasks in Japanese documents.

A biodiversity text classification model fine-tuned based on EnvironmentalBERT-base, specializing in ESG/nature-related biodiversity text detection

This is an 80M parameter M2-BERT pre-trained model, supporting sequences up to 32,768 tokens in length, specifically optimized for long-context retrieval tasks.

GHisBERT is a BERT-based model, specifically trained from scratch for historical German data, covering all documented developmental stages of the German language.

A BERT-based named entity recognition model specialized in labeling person names, organization names, and US address information

A LUKE model improved from Japanese BERT, specifically optimized for Japanese named entity recognition tasks

EconoBert is a model fine-tuned on economics domain datasets based on bert-base-uncased, suitable for NLP tasks in economics, political science, and finance.

GeoLM is a language model designed for detecting toponyms from sentences. It is pre-trained on global OpenStreetMap, WikiData, and Wikipedia data and fine-tuned on the GeoWebNews dataset.

NEZHA is a neural contextualized representation model for Chinese understanding, based on the Transformer architecture, developed by Huawei Noah's Ark Lab.

This is a Chinese pre-trained language model based on the BERT architecture, supporting both classical and modern Chinese text processing.

AraBERTMo is an Arabic pre-trained language model based on Google's BERT architecture, supporting fill-mask tasks.

A RoBERTa model pretrained on Korean, suitable for various Korean natural language processing tasks.

Pre-trained Arabic BERT medium language model, trained on approximately 8.2 billion words of Arabic text resources

MuRIL is an open-source BERT model pretrained on 17 Indian languages and their transliterated versions by Google, supporting multilingual representations.

Korean machine reading comprehension model fine-tuned from KLUE BERT-base, trained using AIHub dataset

A sentiment analysis model fine-tuned based on the CAMeLBERT dialect Arabic model, supporting Arabic text sentiment classification

AraBERTMo is an Arabic pre-trained language model based on Google's BERT architecture, supporting fill-mask tasks.

Arabic pre-trained language model based on BERT architecture, supporting masked language modeling tasks

This model is fine-tuned based on multilingual BERT, specifically designed for detecting hate speech in German, using monolingual (English) training data.

AraBERTMo is an Arabic pre-trained language model based on BERT architecture, supporting masked language modeling tasks.