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/alibaba-damo-academy/VCBench
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency
https://github.com/alibaba-edu/simple-effective-text-matching
Simple and Effective Text Matching with Richer Alignment Features
https://github.com/alibaba-edu/simple-effective-text-matching-pytorch
Simple and Effective Text Matching with Richer Alignment Features
https://github.com/alibaba-nlp/webagent
https://github.com/alibaba/EasyTransfer
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
https://github.com/alibabasglab/frcrn
https://github.com/alililia/ms_contrib/tree/main/ascend_transformer
https://github.com/alililia/ms_contrib/tree/main/gpu_transformer
https://github.com/alimpiev/embeddings_persistent_homology
https://github.com/alinstein/X_RAY
https://github.com/allanj/dynamic-pal
https://github.com/allenai/Break
https://github.com/allenai/CommonGen-Eval
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
https://github.com/allenai/FineGrainedRLHF
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
https://github.com/allenai/abductive-commonsense-reasoning
https://github.com/allenai/allenact
https://github.com/allenai/allennlp-models
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/allenai/allennlp-reading-comprehension
https://github.com/allenai/bilm-tf
https://github.com/allenai/break-evaluator
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/entailment_bank
https://github.com/allenai/language_fragments
https://github.com/allenai/natural-instructions-expansion
Cross-Task Generalization via Natural Language Crowdsourcing Instructions
https://github.com/allenai/ruletaker
https://github.com/allenai/scientific-claim-generation
https://github.com/allenai/scifact
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/allenai/visprog
Visual Programming: Compositional visual reasoning without training
https://github.com/alpha-innovator/chartvlm
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
https://github.com/alpha-vllm/lumina-t2x
https://github.com/amaiya/onprem
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/amauriciorr/AubreyBot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/amazon-research/auto-cot
Automatic Chain of Thought Prompting in Large Language Models
https://github.com/amazon-research/fact-check-summarization
Improving Factual Consistency of Abstractive Summarization via Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.