Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 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/tatp22/multidim-positional-encoding
https://github.com/tbmoon/LANL_Earthquake_Prediction
https://github.com/tcnguyen/bert
https://github.com/tcxdgit/MachineTranslation_t2t
https://github.com/tdd-ai/data-collection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/teghub/TurkishNER-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/research/lm_commonsense
https://github.com/tensorops/TransformerX
https://github.com/text-machine-lab/transformerpy
https://github.com/thakur-nandan/beir-colbert
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/thanhlong1997/bert_quora
https://github.com/the-Quert/iNLPfun
https://github.com/theQuert/inlpfun
https://github.com/thecodemasterk/BERT
https://github.com/theneuralbeing/bert-finetuning-webinar
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/thestephencasper/common_claim
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
https://github.com/thethirdwheel/malumagraph
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/threelittlemonkeys/pointer-network-pytorch
https://github.com/thu-keg/omnievent
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/thudm/chatglm
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/thudm/chatglm2-6b
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/thudm/chatglm3
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/thudm/swissarmytransformer
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/thudm/visualglm-6b
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/thuiar/textoir-demo
https://github.com/thumt/THUMT
https://github.com/thunlp-mt/ckd
https://github.com/thunlp/ReInfoSelect
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tianyi-lab/reflection_tuning
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
https://github.com/titu1994/neural-architecture-search
https://github.com/tjrwlgns1198/ChatBot
https://github.com/tlatkowski/multihead-siamese-nets
Adversarial NLI: A New Benchmark for Natural Language Understanding
https://github.com/tmalbonph/tcbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.