Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 42
Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.
Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/leandromineti/ml-curriculum
https://github.com/leandromineti/ml-knowledge-graph
https://github.com/leehyunjun-seoultech/seoultech
https://github.com/leejieun51/transformer
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/lemonhu/ner-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lenacabrera/gb_mnmt
https://github.com/lennonzurich/lalala
https://github.com/leolee99/CLIP_ITM
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/leonguertler/textarena
GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents
https://github.com/leozhoujf/DataSciComp
https://github.com/levis0045/snips-nlu
https://github.com/lexmen318/metaCAT
https://github.com/lidongliang666/cv_deep_learning
https://github.com/lif31up/attention-is-all-you-need
https://github.com/lif31up/extended-BERT-for-low-rank-adaption
https://github.com/linjieli222/hero_video_feature_extractor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/linlei1214/SITS-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/linyanglee/token-aware-vat
TAVAT: Token-Aware Virtual Adversarial Training for Language Understanding
https://github.com/linzwcs/aft
Mixture-of-Agents Enhances Large Language Model Capabilities
https://github.com/liqichen6688/duo-attention
https://github.com/littell/NamedTransformer
https://github.com/littlewine/zeco2
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/liucongg/chatglm-finetuning
https://github.com/liuqiangict/lamb_optimizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lizzylizzylizzy/evolvingattention
https://github.com/lkfo415579/MT-Readling-List
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/llx666/git_demo
https://github.com/lonePatient/BERT-chinese-text-classification-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/Bert-Multi-Label-Text-Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/ERNIE-text-classification-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/bert-sentence-similarity-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/longbowking/bert
https://github.com/lorenlugosch/pretrain_speech_model
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.