Papers with Code 아카이브
Question Answering
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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/pwc-1/Paper-9/tree/main/5/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/pwc-1/Paper-9/tree/main/6/CAJ/src/models
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
https://github.com/pwc-1/Paper-9/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/pwc-1/Paper-9/tree/main/llava_next
LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
https://github.com/pwc-1/Paper-9/tree/main/longt5
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/pwc-1/Paper-9/tree/main/luke
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/pytorch/rl
https://github.com/pytorch/text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qapitan/babi-marcus
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/qbxlvnf11/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/contextualized-embedding-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/pretrained-language-model-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qema/orca-py
https://github.com/qhduan/CPM-LM-TF2
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/qihoo360/360zhinao
https://github.com/qingyun-wu/autogen-eval
MathChat: Converse to Tackle Challenging Math Problems with LLM Agents
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/qirui-chen/MultiHop-EgoQA
https://github.com/qiujihao19/artemis
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
https://github.com/qizekun/ShapeLLM
https://github.com/qq326823564/LSDNN
MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks
https://github.com/qugank/pqa.github.io
https://github.com/qwenlm/qwen1.5
https://github.com/qwenlm/qwen2.5-vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/qwopqwop200/GPTQ-for-LLaMa
https://github.com/qxb-tyust/location
https://github.com/qywu/Chinese-GPT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qywu/memformers
https://github.com/r-bakes/math_language_processing
https://github.com/ra1nty/sru-naive
https://github.com/radi-cho/gatedtabtransformer
https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch
https://github.com/radzim/obfuscated
Measuring Mathematical Problem Solving With the MATH Dataset
https://github.com/rah4927/lean-dojo-mew
Measuring Mathematical Problem Solving With the MATH Dataset
https://github.com/rahul-1996/KGraphs-QA
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.