https://github.com/tencent-ailab/metalogic
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation
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/tensorflow/models/blob/master/official/nlp/tasks/electra_task.py
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/tensorflow/models/tree/master/official/nlp
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tensorflow/models/tree/master/official/nlp/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/tensorflow/models/tree/master/official/nlp/modeling/layers
https://github.com/tensorflow/models/tree/master/official/nlp/projects/bigbird
https://github.com/tensorops/TransformerX
https://github.com/terrierteam/pyterrier_rag
https://github.com/text-machine-lab/transformerpy
https://github.com/texttron/tevatron
https://github.com/tgebhart/sheaf_kg_transind
https://github.com/thakur-nandan/beir-colbert
https://github.com/thakur-nandan/income
Unsupervised Dense Information Retrieval with Contrastive Learning
https://github.com/thanhlong1997/bert_quora
https://github.com/thaolmk54/LOGNet-VQA
https://github.com/the-Quert/iNLPfun
https://github.com/theQuert/inlpfun
https://github.com/thecodemasterk/BERT
https://github.com/thecodemasterk/Text-to-Text-transfer-transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thefonseca/factorsum
https://github.com/theneuralbeing/bert-finetuning-webinar
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/theophilee/learner-performance-prediction
https://github.com/theophilegervet/learner-performance-prediction
https://github.com/theoremone/llm-benchmarker-suite
https://github.com/therne/dmn-tensorflow
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/thethirdwheel/malumagraph
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/thezingaro/multimodal-side-tuning
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
https://github.com/thinkwee/DPP_CNN_Summarization
https://github.com/thomasscialom/summa-qa
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models
https://github.com/thomlake/pytorch-attention
https://github.com/thtang/CheXNet-with-localization
https://github.com/thu-keg/korc
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/thu-keg/omnievent
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/thu-keg/probtree
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments
https://github.com/thudm/agenttuning
https://github.com/thudm/chatglm2-6b
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.