# Natural Language Understanding

A conversational embedding model optimized for e-commerce search, fine-tuned based on Stella Embed 400M v5, excelling at understanding natural language queries and matching relevant products

Transformers is an open-source library provided by Hugging Face for natural language processing (NLP) tasks, supporting various pretrained models.

A pretrained Transformer model for robotic control tasks, supporting basic functions such as motion planning and object grasping

Pony Diffusion V6 is a versatile SDXL fine-tuned model capable of generating various visuals of furry, beast, or humanoid forms, supporting both SFW and NSFW content.

Insufficient model information, unable to provide specific introduction

This model is built based on the Transformers library, and its specific functions and uses require further information for confirmation.

This model is a text classification model fine-tuned on the boolq dataset based on microsoft/deberta-v3-large, designed for answering boolean questions.

InstructBLIP is a vision instruction tuning model based on BLIP-2, using Flan-T5-xl as the language model, capable of generating descriptions based on images and text instructions.

Based on the gpt2-large architecture, fine-tuned on 2.58 million instruction-tuning samples, this 774M-parameter language model is suitable for natural language instruction response tasks

Urdu language model based on the transformers library, suitable for natural language processing tasks.

A BERT model fine-tuned on the SNLI corpus for predicting semantic similarity scores between two sentences.

DeBERTa is an improved BERT model that enhances performance through a disentangled attention mechanism and an enhanced masked decoder, surpassing BERT and RoBERTa in multiple natural language understanding tasks.

DeBERTa V2 XXLarge is an enhanced BERT variant based on the disentangled attention mechanism, surpassing RoBERTa and XLNet in natural language understanding tasks with 1.5 billion parameters

A Chinese-oriented XLNet pretrained model aimed at enriching Chinese natural language processing resources and providing diverse Chinese pretrained model options.

DeBERTa is an improved BERT model based on the disentangled attention mechanism and enhanced masked decoder, excelling in multiple natural language understanding tasks.

DeBERTa V2 XXLarge is an improved BERT model based on disentangled attention and enhanced mask decoding, with 1.5 billion parameters, surpassing BERT and RoBERTa performance on multiple natural language understanding tasks

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

DeBERTa V2 XLarge is an enhanced natural language understanding model developed by Microsoft, which improves the BERT architecture through a disentangled attention mechanism and enhanced masked decoder, outperforming BERT and RoBERTa on multiple NLU tasks.

DeBERTa V2 XXLarge is an enhanced natural language understanding model developed by Microsoft, which improves the BERT architecture through a disentangled attention mechanism and enhanced masked decoder, achieving SOTA performance on multiple NLP tasks.

DeBERTa improves upon BERT and RoBERTa models with a disentangled attention mechanism and enhanced masked decoder, demonstrating superior performance in most natural language understanding tasks.

MiniLM is a compact and efficient pre-trained language model, compressed through deep self-attention distillation technology, suitable for language understanding and generation tasks.

DeBERTa-V2-XXLarge is an improved BERT model based on the disentangled attention mechanism and enhanced masked decoder, excelling in multiple natural language understanding tasks.

DeBERTaV3 is an improved version of the DeBERTa model proposed by Microsoft, which enhances efficiency through ELECTRA-style gradient-disentangled embedding sharing pretraining method, demonstrating excellent performance in natural language understanding tasks.

DeBERTa is an enhanced BERT decoding model based on the disentangled attention mechanism, surpassing the performance of BERT and RoBERTa on multiple natural language understanding tasks through improved attention mechanisms and enhanced masked decoders.

DeBERTa is an enhanced BERT decoding model based on the disentangled attention mechanism, improving upon BERT and RoBERTa models, and excels in natural language understanding tasks.

A large-scale generative language model developed by OpenAI, capable of understanding and generating natural language text

DeBERTaV3 is an improved pre-trained language model based on DeBERTa, which enhances efficiency through gradient-disentangled embedding sharing in ELECTRA-style pretraining and excels in natural language understanding tasks.

IndoBERT is a BERT model variant tailored for the Indonesian language, trained using masked language modeling and next sentence prediction objectives. The Lite version is a lightweight model suitable for resource-constrained environments.

An enhanced BERT decoding model based on the decoupled attention mechanism, a large-scale version fine-tuned on the MNLI task.

DeBERTa is an enhanced BERT improvement model based on the disentangled attention mechanism. With 160GB of training data and 1.5 billion parameters, it surpasses the performance of BERT and RoBERTa in multiple natural language understanding tasks.

DeBERTa is an enhanced BERT model based on the disentangled attention mechanism, surpassing BERT and RoBERTa in multiple natural language understanding tasks

DeBERTa is an enhanced BERT decoding model based on the disentangled attention mechanism, which improves upon BERT and RoBERTa models and performs better in most natural language understanding tasks.

DeBERTa is an enhanced BERT decoder model based on the disentangled attention mechanism, excelling in natural language understanding tasks.

DeBERTa is an enhanced BERT decoding model based on the disentangled attention mechanism, surpassing BERT and RoBERTa in multiple natural language understanding tasks by improving the attention mechanism and masked decoder.

DeBERTa is an enhanced BERT decoding model based on the disentangled attention mechanism. By improving the attention mechanism and masked decoder, it surpasses the performance of BERT and RoBERTa in multiple natural language understanding tasks.