# Text Understanding

This is a fine-tuned mT5 model for generating questions from Persian text.

This is a transformers model hosted on Hugging Face Hub. Specific functionalities and use cases require further information.

A question-answering model fine-tuned on SpanBERT/spanbert-base-cased, suitable for reading comprehension tasks

MiniRBT is a Chinese small pre-trained model developed based on knowledge distillation technology, optimized for training efficiency using Whole Word Masking.

MiniRBT is a Chinese small pretrained model developed based on knowledge distillation technology, optimized for training efficiency using Whole Word Masking.

MiniRBT is a small Chinese pre-trained model based on knowledge distillation technology, combined with whole word masking, suitable for various Chinese natural language processing tasks.

A Russian Q&A model fine-tuned from cointegrated/rubert-tiny2, suitable for SQuAD-format question answering tasks

This model is a fine-tuned version of distilbert-base-uncased on the SQuAD adversarial dataset, suitable for question-answering tasks.

OPT is an open pre-trained Transformer language model developed by Meta AI, containing 6.7B parameters, designed to advance research in large-scale language models.

A model fine-tuned on Q&A datasets based on Distilled BERT Base, suitable for Q&A tasks

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

A fine-tuned version based on the Chinese RoBERTa-wwm-ext-base model on multiple sentiment analysis datasets

A fine-tuned version based on the distilroberta-base model, suitable for text-related tasks

A model fine-tuned based on bert-base-uncased-wiki, with unspecified specific tasks

This is a RoBERTa-Base QA model fine-tuned on Chinese corpora, suitable for extractive QA tasks.

This is a RoBERTa model trained on 8.8 million Telugu sentences, specifically optimized for Telugu natural language processing tasks.

PERT is a Chinese pre-trained model based on BERT, focusing on improving Chinese text processing capabilities.

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

BERT-based model fine-tuned on Document Visual Question Answering (DocVQA) tasks

A Chinese pre-trained BERT model using whole word masking strategy, designed to accelerate Chinese natural language processing research.

This is a Chinese question generation model based on the MT5 architecture, capable of automatically generating relevant questions from given Chinese text.

A question answering and named entity recognition model fine-tuned on the conll2003 dataset based on distilbert-base-uncased-squad2

An XLNet pre-trained model for Chinese, aimed at enriching Chinese natural language processing resources and providing diversified choices for Chinese pre-trained models.

A fine-tuned version of albert-base-v2 on Q&A datasets, suitable for question-answering tasks

A fine-tuned version of bert-base-uncased model for Q&A tasks, suitable for English question answering

DistilBERT is a distilled version of the BERT base model, maintaining similar performance while being more lightweight and efficient, suitable for natural language processing tasks such as sequence classification and token classification.

This is a compact version of distilbert-base-multilingual-cased, specifically designed for Portuguese, while maintaining the accuracy of the original model.

Est-RoBERTa is a monolingual Estonian BERT-like model based on the RoBERTa architecture, trained on 2.51 billion Estonian vocabulary tokens.

Dutch BERT model fine-tuned based on GroNLP/bert-base-dutch-cased