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/mgmalek/efficient_cross_entropy

Mistral 7B

https://github.com/mhagiwara/nanigonet

Language Modeling with Gated Convolutional Networks

https://github.com/mhjabreel/CharCnn_Keras

Character-Aware Neural Language Models

https://github.com/mhmdsabry/Sentiment_Analysis

Improving Generalization Performance by Switching from Adam to SGD

https://github.com/michael-wzhu/mpnet_zh

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/michaelfarrell76/End-To-End-Generative-Dialogue

LSTM based Conversation Models

https://github.com/mickeystroller/R-BERT

Enriching Pre-trained Language Model with Entity Information for Relation Classification

https://github.com/microsoft/CyBERTron-LM

Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling

https://github.com/microsoft/MSMARCO-OpenKP

Open Domain Web Keyphrase Extraction Beyond Language Modeling

https://github.com/microsoft/MT-DNN

A Hybrid Neural Network Model for Commonsense Reasoning

https://github.com/microsoft/OpenKP

Open Domain Web Keyphrase Extraction Beyond Language Modeling

https://github.com/microsoft/PythonProgrammingPuzzles

Evaluating Large Language Models Trained on Code

https://github.com/microsoft/dialoglm

UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

https://github.com/microsoft/guidance

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

https://github.com/microsoft/kear

Fusing Context Into Knowledge Graph for Commonsense Question Answering

https://github.com/microsoft/kid

The Curious Case of Neural Text Degeneration

https://github.com/microsoft/mttl

Towards Modular LLMs by Building and Reusing a Library of LoRAs

https://github.com/microsoft/only_train_once

LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

https://github.com/microsoft/recommenders

Variational Autoencoders for Collaborative Filtering

https://github.com/microsoft/turing-experiments

Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

https://github.com/microsoft/unilm/tree/master/infoxlm

InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training

https://github.com/microsoft/verus-proof-synthesis

AutoVerus: Automated Proof Generation for Rust Code

https://github.com/mikekestemont/pie

Pointer Sentinel Mixture Models

https://github.com/mikel-brostrom/boxmot

CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels

https://github.com/milmor/GPT

Language Models are Few-Shot Learners

https://github.com/mim-solutions/bert_for_longer_texts

Longformer: The Long-Document Transformer

https://github.com/mim-solutions/roberta_for_longer_texts

Longformer: The Long-Document Transformer

https://github.com/minbeomkim/CriticControl

Critic-Guided Decoding for Controlled Text Generation

https://github.com/mindspore-ai/contrib/tree/master/intern/Knowing-When-to-Look-Adaptive-Attention

Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning

https://github.com/mindspore-ai/models/tree/master/official/nlp/cpm

CPM: A Large-scale Generative Chinese Pre-trained Language Model

https://github.com/mindspore-ai/models/tree/master/official/nlp/gpt

Language Models are Few-Shot Learners

https://github.com/mindspore-ai/models/tree/master/official/nlp/mass

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/mindspore-ai/models/tree/master/research/nlp/luke

LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

https://github.com/mindspore-courses/DeepNLP-models-MindSpore

A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

https://github.com/mindspore-lab/mindocr

Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

https://github.com/mingdachen/bilm-tf

Deep contextualized word representations

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