# Question Answering System

Super Rainbow Cuckoo is a small-scale information extraction model based on the Next Token Extraction (NTE) paradigm, achieving efficient information extraction by mimicking the prediction methods of large language models.

A question-answering model fine-tuned on distilbert-base-uncased-distilled-squad, achieving an F1 score of 81.33 on the evaluation set

ProTrix is a model designed for table and sentence contextual planning and reasoning, focusing on joint reasoning tasks involving tabular and textual data.

ReasTAP is a pre-trained model for table reasoning, which injects table reasoning skills through synthetic reasoning examples and is fine-tuned on the WikiTableQuestions dataset

This model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on the SQuAD dataset, primarily used for question answering tasks.

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

This model is a fine-tuned version of DistilBERT on the SQuAD question-answering dataset, designed for QA tasks.

A question-answering model fine-tuned on the SQuAD 2.0 dataset based on the ALBERT base model

A question-answering model fine-tuned on the SQuAD dataset based on distilbert-base-uncased

A question-answering model fine-tuned based on the roberta-base model, trained on SQuAD format datasets

This model is a question-answering model fine-tuned on the SQuAD v2 dataset based on DistilBERT, suitable for reading comprehension tasks.

A question-answering model fine-tuned on the SQuAD 2.0 dataset based on the ALBERT base model

Model fine-tuned on the squad dataset based on facebook/bart-base, suitable for question answering tasks

A question-answering model fine-tuned on the SQuAD dataset based on facebook/bart-base

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

This is a BERT model fine-tuned on the SQuAD dataset for question answering tasks.

A fine-tuned version of the MobileBERT model on the SQuADv1 question answering dataset, optimized for QA tasks

This model is a question-answering model fine-tuned on the SQuAD v2 dataset based on DistilBERT, suitable for reading comprehension tasks.

A question-answering model based on DistilBERT, fine-tuned on the SQuAD v2 dataset, suitable for QA tasks

A question-answering model fine-tuned on the squad_v2 dataset based on the distilbert-base-uncased model

This model is a fine-tuned version of deepset/roberta-base-squad2, suitable for question answering tasks.

BERT base model fine-tuned on TriviaQA dataset for open-domain question answering tasks

A question-answering model based on DistilBERT, fine-tuned on the SQuAD dataset for extractive question answering tasks.

This model is a fine-tuned version of the DistilBERT base model on the SQuAD question answering dataset, specifically designed for question answering tasks.

This model is a fine-tuned version of the DistilBERT base model on the SQuAD question answering dataset, designed for question answering tasks.

A Transformer-based pre-trained encoder-decoder model designed for Vietnamese and English, supporting summarization, translation, and question answering tasks.

A question-answering model fine-tuned on the SQuAD 2.0 dataset based on RoBERTa-base, designed to answer questions based on given text

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

This model is a question-answering model based on the DeBERTa architecture, fine-tuned on the SQuAD1, AQA, and NewsQA datasets.

This model is a fine-tuned version of DeBERTa-base on the SQuAD question answering dataset, designed for automatic question answering tasks

A BERT-based question answering model fine-tuned on the SQuAD dataset

A question answering model fine-tuned on the SQuAD dataset based on ALBERT-xxlarge-v2

A question-answering model fine-tuned on the SQuAD dataset based on RoBERTa-base

This model is a question-answering model fine-tuned on the SQuAD dataset based on bert-base-uncased

LongT5 is a text-to-text transformation model based on the T5 architecture, employing transient global attention mechanism for efficient processing of long sequence inputs

A question-answering model fine-tuned on the SQuAD dataset based on the DistilBERT base model

This model is a question-answering model fine-tuned on the SQuAD dataset based on DistilBERT, suitable for reading comprehension tasks.

CANINE-C is a character-based pre-trained Transformer model developed by Google, specifically designed for question answering tasks involving long text sequences.

This model is a question-answering model based on the ALBERT architecture, fine-tuned on the SQuAD 2.0 dataset for reading comprehension tasks.

This model is a fine-tuned version of DistilBERT on the SQuAD question answering dataset, designed for QA tasks.