# Natural language understanding

RouWei-0.6 is a text-to-image model fine-tuned extensively on Illustrious-xl-early-release-v0, specializing in anime-style image generation with exceptional prompt-following capability and aesthetic performance.

Llama-3.1-Centaur-70B is a cognitive foundation model capable of predicting and simulating human behavior in any behavioral experiment described in natural language.

BERT-small based model for classifying code and natural language with over 99% accuracy

This is a BERT-based zero-shot text classification model, specifically designed for zero-shot classification tasks, trained using the aspect-normalized UTCD dataset.

An open-domain question answering model trained on the complete Natural Questions (NQ) dataset, capable of answering various factual questions

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model based on the Transformer architecture, developed by Google.

This is a 710M parameter DeBERTa-v2 model focused on Chinese natural language understanding tasks. It is pre-trained using the whole-word masking method, providing strong support for the Chinese NLP field.

DistilBart-MNLI is a lightweight version distilled from bart-large-mnli using teacher-free distillation technology, maintaining high accuracy while reducing model complexity.

IndoBERT is an advanced language model based on BERT, specifically designed for Indonesian, trained with masked language modeling and next sentence prediction objectives.

DistilBart-MNLI is a distilled version of BART-large-MNLI, using teacher-free distillation technology, significantly reducing model size while maintaining high performance.

DistilBart-MNLI is a distilled version of bart-large-mnli using teacher-free distillation techniques, achieving performance close to the original model while being more lightweight.

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

IndoBERT is an advanced Indonesian language model based on the BERT model, trained with masked language modeling and next - sentence prediction objectives.

IndoBERT is the state - of - the - art Indonesian language model based on the BERT model, trained through masked language modeling and next - sentence prediction objectives.

DeBERTa v3 xsmall is an improved natural language understanding model developed by Microsoft, which enhances performance through decoupled attention mechanisms and enhanced masked decoders, surpassing RoBERTa in multiple NLU tasks.

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

MacBERT is an improved Chinese BERT model that employs M as a corrective masked language model pre-training task, alleviating the inconsistency between pre-training and fine-tuning stages.

DeBERTaV3 improves upon DeBERTa with ELECTRA-style pre-training and gradient-disentangled embedding sharing techniques, excelling in natural language understanding tasks

DeBERTa-v3 XL is a large-scale pre-trained language model based on the Transformer architecture, specifically fine-tuned for natural language inference tasks.

A fine-tuned version based on the xlm-roberta-large-en-ru model on the MNLI dataset, supporting natural language inference tasks in English and Russian.

This is a versatile large language model capable of understanding and generating natural language text