https://github.com/vijayakuruba/IFT6010_Applying_Answer-Clue-Style_Approach_to_VQG
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/vikashranjan/transformer
https://github.com/vikparuchuri/marker
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
https://github.com/vikrantmane7781/detectroon2
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
https://github.com/vilm-ai/viet-llm-eval
https://github.com/vinairesearch/phogpt
https://github.com/vinayak19th/Brevis-2.0
https://github.com/vinayprabhu/X-is-all-you-need
https://github.com/vincentschen/limited-label-scene-graphs
https://github.com/vineet2104/StanceDetection-CS626
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
https://github.com/vinhkhuc/MemN2N-babi-python
https://github.com/viniciusoliveirasd/bert-applications
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/virajprabhu/premise-emnlp17
The Promise of Premise: Harnessing Question Premises in Visual Question Answering
https://github.com/vishnuanil122/Bert-Binary-Text-Classifier
https://github.com/vision-cair/minigpt-4
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
https://github.com/visioncortex/vtracer
Visually Descriptive Language Model for Vector Graphics Reasoning
https://github.com/vkartik97/habitat-challenge-two-docker
https://github.com/vladsandulescu/hatefulmemes
https://github.com/vlm-rl/ocean-r1
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
https://github.com/vmurahari3/visdial-bert
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/vmware-labs/bert-pretraining
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/voicy-ai/DialogStateTracking
https://github.com/volcengine/vegiantmodel
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vortezwohl/Autono
https://github.com/voxmenthe/OmniNet_1
OmniNet: A unified architecture for multi-modal multi-task learning
https://github.com/vpj/jax_transformer
https://github.com/vulus98/rethinking-attention
https://github.com/vvvm23/TchAIkovsky-Legacy
https://github.com/vvvm23/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/wDaniec/pytorch-RNN
https://github.com/wDaniec/recurrent-relational-network
https://github.com/walkacross/transformer-pytorch
https://github.com/wan2000/mc-mot
https://github.com/wangmz15/Chinese-Error-Correction-with-THUMT
https://github.com/wangrongsheng/caregpt
https://github.com/wasimusu/MachineRC
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.