# RoBERTa Optimization

A BERT-fine-tuned named entity recognition model specifically designed for the TCM field, achieving leading performance in NER tasks within this domain.

PhoBERT is the most advanced pretrained language model for Vietnamese, optimized based on RoBERTa, and excels in various Vietnamese NLP tasks.

A RoBERTa-based Chinese ChatGPT-generated text detection model designed to distinguish between human-written and ChatGPT-generated text.

A legal domain language model continuously pre-trained on the LeXFiles legal corpus based on the RoBERTa large model

RelBERT is a model based on RoBERTa-large, specifically designed for relation embedding tasks, trained on the SemEval-2012 Task 2 dataset using NCE (Noise Contrastive Estimation).

RoBERTa model optimized for the Contract Understanding Atticus Dataset (CUAD) Q&A task, excelling in legal contract review tasks

A model fine-tuned based on distilroberta-base, with specific uses and training data not clearly stated

Optimized version fine-tuned on multiple sentiment analysis datasets based on Chinese RoBERTa-wwm-ext-large model

This is a RoBERTa-based extractive question answering model, specifically trained on the SQuAD 2.0 dataset, suitable for English Q&A tasks.

A RoBERTa-base model fine-tuned for financial domain Q&A tasks, specifically designed for customized Q&A systems in banking, finance, and insurance sectors.

A model trained for named entity recognition tasks using the MIT movie dataset based on the Roberta Base model

A simplified Chinese sentence embedding encoding model based on simple contrastive learning, using the Chinese RoBERTa WWM extended version as the pre-trained model.

A multilingual text classification model fine-tuned on the amazon_reviews_multi dataset based on XLM-RoBERTa-base

Supervised SimCSE model based on RoBERTa-large for sentence embedding and feature extraction tasks.

A fine-grained sentiment classification model based on the RoBERTa architecture, trained on the GoEmotions dataset, capable of recognizing 28 emotion categories.

RuBioRoBERTa is a RoBERTa model pretrained for Russian biomedical text, specifically designed for natural language processing tasks in the biomedical domain.

A model fine-tuned on the RoBERTa-base for legal contract Q&A tasks, specifically designed for contract review

A text classification model based on the RoBERTa architecture, fine-tuned on the SST-2 sentiment analysis task with an accuracy of 94.5%

This is a model based on the RoBERTa pre-trained model, using the UD_English dataset for POS tagging and dependency parsing.