Papers with Code 아카이브

Language Modeling

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코드

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

https://github.com/jvdbogae/artverc

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/jxmorris12/unsupervised-text-deidentification

Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order

https://github.com/jy0205/Pyramid-Flow

Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

https://github.com/jyp1111/sentiment_analysis

How to Fine-Tune BERT for Text Classification?

https://github.com/jzhang38/EasyContext

Ring Attention with Blockwise Transformers for Near-Infinite Context

https://github.com/jzhang38/longmamba

Extending Context Window of Large Language Models via Positional Interpolation

https://github.com/jzilly/RecurrentHighwayNetworks

Recurrent Highway Networks

https://github.com/jzmo/espnet

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

https://github.com/kafura-kafiri/tf2-elmo

Pointer Sentinel Mixture Models

https://github.com/kahotsang/image-captioning

Where to put the Image in an Image Caption Generator

https://github.com/kaist-dmlab/BioNER

Deep contextualized word representations

https://github.com/kaituoxu/Listen-Attend-Spell

Listen, Attend and Spell

https://github.com/kajyuuen/daaja

Data Augmentation using Pre-trained Transformer Models

https://github.com/kakaobrain/honeybee

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

https://github.com/kamanphoebe/look-into-moes

Mixtral of Experts

https://github.com/kanishkamisra/minicons

A Better Way to Do Masked Language Model Scoring

https://github.com/kanishkg/synchromesh

Synchromesh: Reliable code generation from pre-trained language models

https://github.com/karpathy/build-nanogpt

Language Models are Few-Shot Learners

https://github.com/karpathy/llm.c

Language Models are Few-Shot Learners

https://github.com/karta282950/bert-multiclass

Semi-supervised Sequence Learning

https://github.com/kaushaltrivedi/fast-bert

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/kaviezhang/meshmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/kc-ml2/darts

DARTS: Differentiable Architecture Search

https://github.com/kefirski/hybrid_rvae

A Hybrid Convolutional Variational Autoencoder for Text Generation

https://github.com/kefirski/pytorch_RVAE

Generating Sentences from a Continuous Space

https://github.com/kehanlu/DeSTA2

DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data

https://github.com/keithRebello/ULMFiT_sentiment_analysis

Universal Language Model Fine-tuning for Text Classification

https://github.com/keiyamashita111/aaproject

Semi-supervised Sequence Learning

https://github.com/kelly2016/multi-label-bert

Semi-supervised Sequence Learning

https://github.com/kensho-technologies/timtc_vocabs_models

The MiniPile Challenge for Data-Efficient Language Models

https://github.com/keonlee9420/Comprehensive-Transformer-TTS

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/kevin666aa/flaml

Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference

https://github.com/keyhsw/chemllm

ChemLLM: A Chemical Large Language Model

https://github.com/keyonvafa/career-code

Rationales for Sequential Predictions

https://github.com/khanrc/pt.darts

DARTS: Differentiable Architecture Search

https://github.com/kheeong/XLM_OWN

Cross-lingual Language Model Pretraining

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