Papers with Code 아카이브

Question Answering

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하위 태스크: 19
평가표: 153
보존된 평가 기록: 1,781

Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.

( Image credit: SQuAD )

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MINGG2333/bert_ms

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/MLH-Fellowship/Social-BERTerfly

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted

Attention Is All You Need

https://github.com/MS-Mind/MS-Code-01/tree/main/soft_masked_bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/MS-P3/code5/tree/main/bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/MS-P3/code5/tree/main/gpt2

https://github.com/MS-P3/code5/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama2

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/mobilebert

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

https://github.com/MS-P3/code5/tree/main/t5

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/MS-P3/code6/tree/main/finetune

Finetuned Language Models Are Zero-Shot Learners

https://github.com/MS-P3/code7/tree/main/van

VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs

https://github.com/MS-P3/code7/tree/main/xlm_roberta_xl

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/MS-P3/code7/tree/main/xlnet

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/MaZhiyuanBUAA/bert-tf1.4.0

Attention Is All You Need

https://github.com/Machine-Learning-for-Everyone-ML4E/CoP-Code-of-Papers

A Self-Attentive model for Knowledge Tracing

https://github.com/MalteHB/-l-ctra

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/Maluuba/mctest-model

A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data

https://github.com/ManasRMohanty/DS5500-capstone

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/MansMeg/IntroML

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/Maple728/MTNet

A Memory-Network Based Solution for Multivariate Time-Series Forecasting

https://github.com/Maple728/transformer

Attention Is All You Need

https://github.com/MarcBS/keras

Attention Is All You Need

https://github.com/MarcosGrzeca/drunktweets

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/MarkHershey/arxiv-dl

SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

https://github.com/MashaKubyshina/learning_ML

Attention Is All You Need

https://github.com/MatthewSwahn/news-headlines-sarcasm-detector

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/Matthewdowney18/Transformer_Dialogue

Attention Is All You Need

https://github.com/Maz101/Bert

Attention Is All You Need

https://github.com/Maz101/pretrainingdataset

Attention Is All You Need

https://github.com/McGill-NLP/topiocqa

TopiOCQA: Open-domain Conversational Question Answering with Topic Switching

https://github.com/MeRajat/SolvingAlmostAnythingWithBert

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Graph Attention Networks

https://github.com/Mehrab-Tanjim/enforce-reasoning

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

https://github.com/Mehrab-Tanjim/vilbert-rationalization

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

https://github.com/Messham87/COVID_GAT

Graph Attention Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.