https://github.com/pwc-1/Paper-8/tree/main/data2vec
Decoder Tuning: Efficient Language Understanding as Decoding
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/pwc-1/Paper-8/tree/main/dbrx
https://github.com/pwc-1/Paper-8/tree/main/deberta
https://github.com/pwc-1/Paper-8/tree/main/deberta_v2
https://github.com/pwc-1/Paper-9/tree/main/1/table_transformer
TableFormer: Robust Transformer Modeling for Table-Text Encoding
https://github.com/pwc-1/Paper-9/tree/main/1/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/pwc-1/Paper-9/tree/main/4/Translation-Invariant/model
https://github.com/pwc-1/Paper-9/tree/main/ibert
https://github.com/pytorch/text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/contextualized-embedding-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/pretrained-language-model-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qywu/Chinese-GPT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch
https://github.com/rajlm10/Chandler
https://github.com/ramanakshay/clip
Learning Transferable Visual Models From Natural Language Supervision
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/redcaps-dataset/redcaps-downloader
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/redevaaa/Transformer-for-EEG
https://github.com/reppy4620/Dialog
https://github.com/rhythmcao/text2sql-lgesql
Dual Learning for Semi-Supervised Natural Language Understanding
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/rickyHong/Google-BERT-repl
https://github.com/ricsinaruto/NeuralChatbots-DataFiltering
https://github.com/ricsinaruto/dialog-eval
https://github.com/rinnakk/japanese-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/rishavbhurtel/sentiment-classifier
https://github.com/rita-nlp/italic
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.