# Q&A System

This model is based on a decoder fusion architecture, using the t5-large checkpoint, trained on the Natural Questions dataset for Q&A system tasks.

A model fine-tuned on the NaturalQuestions dataset based on microsoft/MiniLM-L12-H384-uncased, suitable for question-answering tasks

Electra-qa is a Q&A model based on the ELECTRA architecture, suitable for question-answering tasks.

Lightweight Q&A model based on DistilBERT, fine-tuned on the SQuAD dataset

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

A Q&A model fine-tuned on the TriviaQA dataset based on bert-base-uncased

A Q&A model fine-tuned on the SQuAD dataset based on the DistilBERT model

This model is a fine-tuned version of DistilBERT on the SQuAD Q&A dataset, suitable for question-answering tasks.

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

This model is a fine-tuned version of bert-base-uncased on the squad_v2 dataset, suitable for question-answering tasks.

A Q&A model based on DistilBERT, fine-tuned on the SQuAD dataset for reading comprehension tasks

This model is a question-answering model fine-tuned on the SQuAD dataset based on DistilBERT, designed for extractive question-answering tasks.

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

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

This model is a fine-tuned version of DistilBERT on the SQuAD v2 question-answering dataset, designed for Q&A tasks.

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

This is a base model based on the BERT architecture, specifically trained on the SQuAD v2 dataset, suitable for question-answering tasks.

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

A Q&A model based on the MobileBERT architecture, fine-tuned for the SQuAD v2 dataset

This model is a question-answering model fine-tuned on the SQuAD 2.0 dataset based on RoBERTa-base, excelling in reading comprehension tasks.

A Q&A model fine-tuned on the SQuAD 2.0 dataset based on the RoBERTa-base model

A Q&A model fine-tuned on the SQuAD2.0 dataset based on the BERT-Medium architecture, designed for resource-constrained environments

This model is a lightweight version based on DistilBERT, fine-tuned on the SQuAD question-answering dataset for Q&A tasks.

A Q&A system model based on T5-small architecture, supporting extractive Q&A tasks and capable of handling both answerable and unanswerable scenarios

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

This is a pruned and optimized BERT Q&A model, retaining 38.1% of the original model's weights, fine-tuned on the SQuAD1.1 dataset, supporting English Q&A tasks.

This model is a fine-tuned version of distilbert-base-uncased on the SQuAD question-answering dataset, specifically designed for Q&A system tasks.

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

BERT-Small is a compact BERT model developed by Google Research, fine-tuned on the SQuAD 2.0 dataset, suitable for Q&A tasks in resource-constrained environments.

A Q&A model fine-tuned on the SQuAD v2.0 dataset based on Electra-small-discriminator, suitable for distinguishing answerable from unanswerable questions

A Q&A model fine-tuned on the SQuAD dataset based on the BERT architecture

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

ALBERT-base-v2 model provided for Stanford CS224n course students, used for SQuAD2.0 Q&A task benchmarking

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

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

A Q&A model fine-tuned on the SQuAD dataset based on the BERT base model

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

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.