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XLM-R
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XLM-R
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Adapter-Hub/Hub
공식 코드https://github.com/Adapter-Hub/adapter-transformers
공식 코드https://github.com/BUTSpeechFIT/BaySMM
공식 코드A Bayesian Multilingual Document Model for Zero-shot Topic Identification and Discovery
https://github.com/KB-Ding/Multi-stage-Distillaton-Framework
공식 코드Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
https://github.com/UBC-NLP/marbert
공식 코드ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
https://github.com/VinAIResearch/BERTweet
공식 코드https://github.com/VinAIResearch/PhoBERT
공식 코드https://github.com/VinAIResearch/PhoNER_COVID19
공식 코드https://github.com/VinAIResearch/ViText2SQL
공식 코드A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese
https://github.com/abteen/americasnli
공식 코드https://github.com/aetherprior/multilingual-sentence-boundary-detection
공식 코드Punctuation Restoration for Singaporean Spoken Languages: English, Malay, and Mandarin
https://github.com/ai4bharat/indicbert
공식 코드https://github.com/alanansell/bistil
공식 코드Distilling Efficient Language-Specific Models for Cross-Lingual Transfer
https://github.com/alexa/massive
공식 코드https://github.com/alibol/dss-project
공식 코드https://github.com/bminixhofer/zett
공식 코드https://github.com/boschresearch/clin_x
공식 코드https://github.com/boschresearch/steps-parser
공식 코드https://github.com/cambridgeltl/BLICEr
공식 코드Improving Bilingual Lexicon Induction with Cross-Encoder Reranking
https://github.com/cambridgeltl/xcopa
공식 코드MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
https://github.com/cardiffnlp/xlm-t
공식 코드XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
https://github.com/cctien/bimultialign
공식 코드Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining
https://github.com/cisnlp/glot500
공식 코드Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages
https://github.com/cisnlp/langsamp
공식 코드https://github.com/coastalcph/mozart
공식 코드Are Pretrained Multilingual Models Equally Fair Across Languages?
https://github.com/coastalcph/mpararel
공식 코드Factual Consistency of Multilingual Pretrained Language Models
https://github.com/csu-signal/xlingual-robustness
공식 코드Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets
https://github.com/d-gurgurov/GrEmLIn-Green-Embeddings-LRLs
공식 코드https://github.com/dsfsi/za-lid
공식 코드From N-grams to Pre-trained Multilingual Models For Language Identification
https://github.com/eftekhar-hossain/CUET_NLP-EACL_2021
공식 코드https://github.com/facebookresearch/XLM
공식 코드https://github.com/facebookresearch/cc_net
공식 코드https://github.com/flagai-open/flagai
공식 코드AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
https://github.com/ghchen18/acl22-sixtp
공식 코드Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
https://github.com/gringham/explainable-metrics-machine-translation
공식 코드Towards Explainable Evaluation Metrics for Natural Language Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.