Tiny Doc Qa Vision Encoder Decoder

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

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