# High-precision Reading Comprehension

This model is a fine-tuned version of Legal-BERT on the SQuAD dataset, specializing in legal domain question-answering tasks.

A question answering system model based on the DeBERTa-v3-xsmall architecture, specifically fine-tuned for the SQuAD2.0 dataset

A QA model fine-tuned on the adversarial_qa dataset based on microsoft/deberta-base

The xlarge version v2 model based on ALBERT architecture, fine-tuned on the SQuAD V2 dataset for Q&A tasks

This model is a question-answering system based on the BERT Base Uncased architecture, fine-tuned on the NewsQA dataset.

This is an English extractive question answering model based on the ELECTRA-base architecture, trained on the SQuAD 2.0 dataset, suitable for question answering tasks.

A question answering system based on the ALBERT-xlarge-v2 model fine-tuned on the SQuAD V2 dataset

XLNet is a pre-trained language model jointly developed by Google and Carnegie Mellon University, fine-tuned on the SQuAD 2.0 question answering dataset

A question-answering model fine-tuned on the SQuAD V2 dataset based on BioBERT v1.1 Pubmed, specifically designed for biomedical QA tasks

This model is based on the RoBERTa architecture and fine-tuned on the SQuAD1.1 dataset, capable of extracting answers to questions from given contexts.

A Portuguese question-answering model trained on BERTimbau Large and fine-tuned on the Portuguese version of SQuAD v1.1, suitable for Portuguese QA tasks.

MobileBERT is a lightweight version of BERT_LARGE, fine-tuned on the SQuAD2.0 dataset for question answering systems.

A two-stage training QA model based on ELECTRA-Large architecture, first trained on synthetic adversarial data and then fine-tuned on SQuAD and AdversarialQA datasets

A question-answering model based on the bert-large-uncased architecture and fine-tuned on the SQuAD2.0 dataset

BETO is a BERT model trained on a large Spanish corpus, this version is fine-tuned for Spanish question answering tasks

A Korean Q&A system fine-tuned based on the xlm-roberta-large-squad2 model, demonstrating excellent performance in KorQuAD evaluations.

This model is a fine-tuned version of microsoft_deberta-large on the SQuAD V1 dataset, primarily used for question answering tasks.

This is a pretrained model based on the XLM-RoBERTa architecture, trained with masked language modeling objectives and fine-tuned on English and Russian question answering datasets.

RoBERTa-based English extractive question answering model, trained on the SQuAD 2.0 dataset, capable of handling both answerable and unanswerable questions