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/paralleldrive/sudolang-llm-support

Prompting Is Programming: A Query Language for Large Language Models

https://github.com/park-cheol/ASR-Conformer

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

https://github.com/park-cheol/ASR-Transformer

Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM

https://github.com/parkervg/allrecipes-bert

VideoBERT: A Joint Model for Video and Language Representation Learning

https://github.com/pascalcarrivain/ksmm

Monarch: Expressive Structured Matrices for Efficient and Accurate Training

https://github.com/pasta41/deception

Pointer Sentinel Mixture Models

https://github.com/patelrajnath/dl4nlp-py

Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text

https://github.com/patelrajnath/rnn4nlp

Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text

https://github.com/patil-suraj/question_generation

Simplifying Paragraph-level Question Generation via Transformer Language Models

https://github.com/pauldevos/python-notes

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/pavel-blinov/RuMedBench

RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining

https://github.com/peizeli96/MHys

Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering

https://github.com/pengbaolin/soloist

SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching

https://github.com/pengchengguo/espnet

Improved training of end-to-end attention models for speech recognition

https://github.com/pengming617/bert_classification

Semi-supervised Sequence Learning

https://github.com/pengshuyuan/Bert

Semi-supervised Sequence Learning

https://github.com/peregilk/north-t5

Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model

https://github.com/phanein/deepwalk

DeepWalk: Online Learning of Social Representations

https://github.com/phanxuanphucnd/Arizona-asr

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/phanxuanphucnd/Felix

Felix: Flexible Text Editing Through Tagging and Insertion

https://github.com/phanxuanphucnd/conformer

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/phbst/tinyrag

In-Context Retrieval-Augmented Language Models

https://github.com/philipphager/baidu-bert-model

Multi-Stage Document Ranking with BERT

https://github.com/philippwirth/awd-lstm-test

Improving Neural Language Models with a Continuous Cache

https://github.com/philippwirth/treelangrnn

Improving Neural Language Models with a Continuous Cache

https://github.com/philschmid/knowledge-distillation-transformers-pytorch-sagemaker

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/phixion/phixion

Language agents achieve superhuman synthesis of scientific knowledge

https://github.com/phnk/D7047E

Representation of linguistic form and function in recurrent neural networks

https://github.com/phsfr/UD_Persian-PerDT

Beheshti-NER: Persian Named Entity Recognition Using BERT

https://github.com/phueb/BabyBertSRL

Multi-Task Deep Neural Networks for Natural Language Understanding

https://github.com/phueb/CHILDES-SRL

Multi-Task Deep Neural Networks for Natural Language Understanding

https://github.com/phueb/unmasked

Masked Language Model Scoring

https://github.com/phunterlau/paper_without_code/tree/main/examples/PoG/

Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs

https://github.com/phykn/film-defect-detection

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

https://github.com/piegu/language-models

MultiFiT: Efficient Multi-lingual Language Model Fine-tuning

https://github.com/piero2c/BitInfer

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

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