Papers with Code 아카이브

Language Modeling

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/barisozmen/deepaugment

Neural Architecture Search with Reinforcement Learning

https://github.com/bcaitech1/p2-klue-Heeseok-Jeong

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bcaitech1/p4-dkt-no_caffeine_no_gain

How to Fine-Tune BERT for Text Classification?

https://github.com/beerzyp/ECAC-Bimodal_audiovisual_recognition

Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text

https://github.com/beerzyp/ECAC-Chain-Fusion

Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text

https://github.com/behavioral-data/cognitive-reframing

Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction

https://github.com/bencrabbe/npdependency

FlauBERT: Unsupervised Language Model Pre-training for French

https://github.com/benedekrozemberczki/karateclub

DeepWalk: Online Learning of Social Representations

https://github.com/benedekrozemberczki/pytorch_geometric_temporal

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

https://github.com/benjamin-mlr/mbert-unseen-languages

When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models

https://github.com/benjaminvdb/110kDBRD

Universal Language Model Fine-tuning for Text Classification

https://github.com/benjaminvdb/DBRD

Universal Language Model Fine-tuning for Text Classification

https://github.com/benkrause/dynamiceval-transformer

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/benprano/Question-Answering-Deep-Learning

End-To-End Memory Networks

https://github.com/benradford/replication_arxiv_1803_10769

Network Traffic Anomaly Detection Using Recurrent Neural Networks

https://github.com/bentoml/openllm

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/benywon/ReCO

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bestend/tf2-bi-lstm-crf-nni

Deep contextualized word representations

https://github.com/bfopengradient/NLP_ROBERTA

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bhavitvyamalik/bert

Semi-supervised Sequence Learning

https://github.com/bhuvanakundumani/sexist_rmks_classifier

Universal Language Model Fine-tuning for Text Classification

https://github.com/bigcode-project/starcoder

Finetuned Language Models Are Zero-Shot Learners

https://github.com/bigdata-ustc/Zero-1-to-3

FedJudge: Federated Legal Large Language Model

https://github.com/bigscience-workshop/bigscience

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

https://github.com/bigscience-workshop/massive-probing-framework

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

https://github.com/bigscience-workshop/promptsource

Language Models are Few-Shot Learners

https://github.com/binbinjiang/cvt-slr

Progressive Multi-Modality Learning for Inverse Protein Folding

https://github.com/binhetech/bert-application

Semi-supervised Sequence Learning

https://github.com/biofm/openbiomed

MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering

https://github.com/bionlu-coling2024/biomed-ner-intent_detection

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

https://github.com/biswa380/t5_multitask_api

Simplifying Paragraph-level Question Generation via Transformer Language Models

https://github.com/bitextor/bicleaner-ai

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/biyoml/End-to-End-Mandarin-ASR

Listen, Attend and Spell

https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT

Listen, Attend and Spell

https://github.com/bjascob/amrlib

AMR Parsing via Graph-Sequence Iterative Inference

https://github.com/blawok/named-entity-recognition

RoBERTa: A Robustly Optimized BERT Pretraining Approach

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.