https://github.com/zhuchen03/maxva
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
Papers with Code 아카이브
Question Answering
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- 평가표
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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/zhuzhicai/SQuAD2.0-Baseline-Test-with-BiDAF-No-Answer
https://github.com/zhuzilin/ring-flash-attention
https://github.com/zihaow123/unimm
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/zijieli-Jlee/Dual-Diffusion
Dual Diffusion for Unified Image Generation and Understanding
https://github.com/zipeiyang/DeepSpeechRecognition
https://github.com/ziqi-zhang/3D-ResNets-PyTorch
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/zitongyang/synthetic_continued_pretraining
https://github.com/ziwang-klvk/cfq-rbmt
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
https://github.com/zixinzeng-jennifer/spanbert_trans
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/zixuwang1996/VQA-reading-list
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
https://github.com/ziye2chen/LLMs-for-Mathematical-Analysis
https://github.com/zjunlp/promptkgc
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
https://github.com/znhy1024/protoco
https://github.com/zongqianwu/st-cot
https://github.com/zphang/lm_evaluation_harness
https://github.com/zrrskywalker/i2p-mae
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
https://github.com/zrrskywalker/point-bind
https://github.com/zsweet/BERT_zsw
https://github.com/zurichnlp/contradecode
https://github.com/zxhhh97/ABot
https://github.com/zyphra/blackmamba
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
https://github.com/zysite/post
https://github.com/zzz47zzz/spurious-forgetting
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction
https://gitlab.com/grounded-sport-convai/goal-baselines
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
https://gitlab.com/viper-purdue/ocil-real-world-food-image-classification
Online Class-Incremental Continual Learning with Adversarial Shapley Value
https://huggingface.co/datasets/Fangjun/RoomSpace
https://huggingface.co/datasets/WaltonFuture/InstructionGPT-4
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
https://huggingface.co/datasets/jjjjh/MM-Instruct
MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
https://huggingface.co/datasets/peiyi9979/Math-Shepherd
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
https://huggingface.co/spaces/daeron/CONVS2S-EVJVQA-DEMO
https://worksheets.codalab.org/worksheets/0x6f181988848243d4918fe48c30cdfe64
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.