Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 153
- 보존된 평가 기록
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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/raidiance/bert-for-radiology
https://github.com/rainbowluocs/openomni
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis
https://github.com/rajatgermany/qa-nlp
https://github.com/rajlm10/Chandler
https://github.com/rakeshbm/QA-using-Torch
https://github.com/ramanakshay/nanogpt
https://github.com/ramanakshay/transformer
https://github.com/rasbot/plot-bot
https://github.com/rasta-nitzsche/Bert-For-Algerian-Dialect-DariBERTo
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/re-search/DocProduct
https://github.com/reactive-systems/circuit-repair
https://github.com/reactive-systems/ml2
https://github.com/recommeddit/labs
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
https://github.com/redevaaa/Transformer-for-EEG
https://github.com/renmada/squeezebert-paddle
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/renmengye/rec-attend-public
https://github.com/replicate/cog_stanford_alpaca
https://github.com/reppy4620/Dialog
https://github.com/reycn/multi-modal-scale
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/ricaelum42/Contextual-Twitter-Sarcasm-Detection
https://github.com/ricardordb/bert
https://github.com/richard-peng-xia/Chinese-Noisy-Text
https://github.com/richard88888/chinese-noisy-text
https://github.com/richarddwang/electra_pytorch
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/richinkabra/CoVe-BCN
https://github.com/rickyHong/Google-BERT-repl
https://github.com/ricolike/mmgnn_textvqa
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text
https://github.com/ricsinaruto/NeuralChatbots-DataFiltering
https://github.com/ricsinaruto/dialog-eval
https://github.com/rijgersberg/geitje
https://github.com/rishavbhurtel/sentiment-classifier
https://github.com/rishikmani/sort-of-clevr
https://github.com/rlqja1107/torch-LLM4SGG
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/rmokady/clip_prefix_caption
Unified Vision-Language Pre-Training for Image Captioning and VQA
https://github.com/roberttwomey/machine-imagination-isea
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.