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Linguistic Acceptability
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Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical.
Image Source: Warstadt et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IntelLabs/academic-budget-bert
공식 코드https://github.com/NervanaSystems/nlp-architect/blob/master/nlp_architect/models/transformers/quantized_bert.py
공식 코드https://github.com/PaddlePaddle/ERNIE
공식 코드ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
https://github.com/amirabaskohi/lm-cppf
공식 코드LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
https://github.com/awslabs/mlm-scoring
공식 코드https://github.com/bramvanroy/fietje-2
공식 코드https://github.com/cjf00000/StatQuant
공식 코드A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
https://github.com/clarin-pl/chatgpt-evaluation-01-2023
공식 코드https://github.com/danchern97/tda4la
공식 코드Acceptability Judgements via Examining the Topology of Attention Maps
https://github.com/dhfbk/itacola-dataset
공식 코드Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus
https://github.com/facebookresearch/SpanBERT
공식 코드SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/google-research/ALBERT
공식 코드ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/google-research/bert
공식 코드BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-research/bigbird
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT
공식 코드TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/huggingface/swift-coreml-transformers
공식 코드DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/huggingface/transformers
공식 코드DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/huggingface/transformers/blob/main/src/transformers/models/squeezebert/modeling_squeezebert.py
공식 코드SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/huhailinguist/colac
공식 코드https://github.com/intellabs/model-compression-research-package
공식 코드https://github.com/jumelet/monotonicity-npi-lm
공식 코드https://github.com/lgalke/cross-architecture-distillation
공식 코드General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings
https://github.com/lijiazheng99/cue
공식 코드https://github.com/ltgoslo/nocola
공식 코드https://github.com/microsoft/DeBERTa
공식 코드https://github.com/namisan/mt-dnn
공식 코드Multi-Task Deep Neural Networks for Natural Language Understanding
https://github.com/osekilab/jcola
공식 코드https://github.com/peteriz/academic-budget-bert
공식 코드https://github.com/pliang279/sent_debias
공식 코드https://github.com/pytorch/fairseq
공식 코드https://github.com/pytorch/fairseq/tree/main/examples/data2vec
공식 코드data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/russiannlp/rucola
공식 코드https://github.com/saattrupdan/scandeval
공식 코드ScandEval: A Benchmark for Scandinavian Natural Language Processing
https://github.com/salesforce/GeDi
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.