The Best 474 Question Answering System Tools in 2025

DistilBERT is a lightweight distilled version of BERT, with 40% fewer parameters, 60% faster speed, while retaining over 95% performance. This model is a question-answering specialized version fine-tuned on the SQuAD v1.1 dataset.

DistilBERT is a lightweight distilled version of BERT, with 40% fewer parameters and 60% faster speed, maintaining over 95% of BERT's performance on the GLUE benchmark. This model is fine-tuned specifically for question answering tasks.

TAPAS is a table question answering model based on the BERT architecture, pre-trained in a self-supervised manner on Wikipedia table data, supporting natural language question answering on table content

A question generation model based on t5-base. Input the answer and context, and output the corresponding question.

A BERT-base-cased based QA pair evaluation model for determining semantic relevance between questions and answers

A document Q&A model based on the MIT License, primarily for testing purposes.

DPR (Dense Passage Retrieval) is a tool and model for open-domain question answering research. This model is a BERT-based question encoder trained on the Natural Questions (NQ) dataset.

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

TAPAS is a Transformer-based table question answering model, pre-trained on Wikipedia table data through self-supervised learning and fine-tuned on datasets like WTQ.

BERT-based Dense Passage Retrieval (DPR) question encoder for open-domain QA research, trained on multiple QA datasets

A model fine-tuned on the RoBERTa-base for legal contract Q&A tasks, specifically designed for contract review

A multilingual Q&A model based on mDeBERTa-v3-base, fine-tuned on the SQuAD2.0 dataset

A T5-base architecture-trained answer-aware question generation model capable of generating corresponding questions based on highlighted answer segments in the text.

An end-to-end question generation model trained on T5-base, capable of automatically generating relevant questions from input text.

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

This is a large-scale discriminator model based on the ELECTRA architecture, specifically fine-tuned for Q&A tasks on the SQuAD2.0 dataset, capable of handling both answerable and unanswerable question scenarios.

This is a question-answering model fine-tuned on the SQuAD v1.1 dataset using knowledge distillation techniques, based on the DistilBERT-base-cased model.

A biomedical domain Q&A model fine-tuned based on BioBERT-v1.1

TAPAS is a BERT-like model based on the Transformer architecture, specifically designed for processing tabular data. It is pre-trained in a self-supervised manner on English Wikipedia table data and fine-tuned on the TabFact dataset to determine whether a sentence is supported or refuted by table content.

This model is based on the T5-base architecture and fine-tuned on the SQuAD v1.1 dataset, specifically designed for question generation tasks.

A compact Q&A model fine-tuned on the SQuAD2.0 dataset based on Google's BERT-Tiny architecture, with a size of only 16.74MB

This is a cross-encoder model based on the ELECTRA architecture, specifically designed for natural language inference (NLI) in question-answering tasks, determining whether a given question can be answered by a specific paragraph.

A Q&A model fine-tuned on the SQuAD v2 dataset based on the ALBERT base v2 architecture, excelling in reading comprehension tasks involving unanswerable questions

TAPAS is a table-based question answering model that handles conversational QA tasks on tabular data through pre-training and fine-tuning.

COMETKiwi is a model for machine translation quality estimation, capable of outputting quality scores based on source text and translated text.

Splinter is a few-shot QA model pre-trained via self-supervised learning, utilizing the Recurrent Span Selection (RSS) objective to simulate the span selection process in extractive QA.

A Portuguese question-answering model fine-tuned on the Portuguese SQUAD v1.1 dataset, based on BERTimbau base, suitable for Portuguese QA tasks.

This is a fine-tuned version of Dicta - IL's dictalm2.0 - instruct model, specifically designed for generating Hebrew question-answer pairs.

A Chinese extractive QA model based on the RoBERTa architecture, suitable for tasks that extract answers from given texts.

TAPEX is a table pre-training model that learns via neural SQL executor, based on the BART architecture, specifically designed for table reasoning tasks.

An extractive QA model fine-tuned on the SQuAD2.0 dataset based on flan-t5-base, capable of handling question-answer pairs including unanswerable questions.

TAPAS is a QA model for tabular data. This tiny version is fine-tuned on the SQA dataset, suitable for table-based QA tasks in conversational scenarios.

Dynamic-TinyBERT is an efficient question answering model that improves inference efficiency through dynamic sequence length reduction, achieving up to 3.3x speedup while maintaining high accuracy.

A Spanish Q&A model optimized via distillation from BETO, more lightweight and efficient than the standard version

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

TAPAS is a tiny Transformer model optimized for table question answering tasks, achieving table comprehension capabilities through intermediate pretraining and chained multi-dataset fine-tuning

A Q&A system model fine-tuned on the SQuAD1.1 dataset based on BERT-base uncased model

A table question answering model based on BERT architecture, enhanced with intermediate pretraining for numerical reasoning, fine-tuned on the SQA dataset

A RoBERTa-based question answering model specialized in inferring answer text, span, and confidence scores given a question and context.

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.

A French Q&A model based on CamemBERT, fine-tuned on three French Q&A datasets: PIAF, FQuAD, and SQuAD-FR

This is the INT8 quantized version of the DistilBERT base uncased model, specifically designed for question answering tasks, optimized for model size and inference speed through post-training static quantization.