Papers with Code 아카이브

Language Modeling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/EduardoTrevino/fail-talms

Evaluating Language Models as Synthetic Data Generators

https://github.com/EightRice/atn_GPT-3

Language Models are Few-Shot Learners

https://github.com/ElectricAlexis/NotaGen

NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms

https://github.com/EleutherAI/GPTNeo

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

https://github.com/EleutherAI/gpt-neo

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm_evaluation_harness

Language Models are Few-Shot Learners

https://github.com/EmergentSystemLabStudent/SpCoSLAM

Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

https://github.com/EmergentSystemLabStudent/SpCoSLAM_Lets

Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

https://github.com/Entropy-xcy/bitnet158

BitNet: Scaling 1-bit Transformers for Large Language Models

https://github.com/EthanWYB/bert-classification

Semi-supervised Sequence Learning

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/nn/utils.py

ReZero is All You Need: Fast Convergence at Large Depth

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/optim.py

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

https://github.com/EuphoriaYan/bert_component

Semi-supervised Sequence Learning

https://github.com/EvLab-MIT/LexiContrastiveGrd

Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling

https://github.com/ExplainableML/cosmos

FLAIR: VLM with Fine-grained Language-informed Image Representations

https://github.com/FLoutione/CPM-mindspore

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

https://github.com/FaskyCC/TextClassification

Semi-supervised Sequence Learning

https://github.com/FengJiaChunFromSYSU/Bert

Semi-supervised Sequence Learning

https://github.com/FerryHuang/SimVLM

SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

https://github.com/FlyingPumba/circuits-benchmark

Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

https://github.com/Franklin-Zhang0/ReasonGen-R1

ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL

https://github.com/FreddeFrallan/Multilingual-CLIP

Language-agnostic BERT Sentence Embedding

https://github.com/FreedomIntelligence/BlenderLLM

HuatuoGPT, towards Taming Language Model to Be a Doctor

https://github.com/FrogTravel/PMLDL

BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/GLUS-video/GLUS

GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation

https://github.com/Gaozhen0816/BERT_QA_For_AILaw

Semi-supervised Sequence Learning

https://github.com/Gaozhen0816/BERT_QA_for_Chinese

Semi-supervised Sequence Learning

https://github.com/Garfield35/Speach-Recognition-Using-Tensorflow

Listen, Attend and Spell

https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data

How to Fine-Tune BERT for Text Classification?

https://github.com/GiuliaLanzillotta/INAS

Neural Architecture Search with Reinforcement Learning

https://github.com/GiuliaLanzillotta/exercises

Generating Sentences from a Continuous Space

https://github.com/Goodideax/lstm-negtive

Recurrent Neural Network Regularization

https://github.com/Goodideax/rnn_neg_efficient

Recurrent Neural Network Regularization

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