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/khumbuai/keras_wiki_lm

Universal Language Model Fine-tuning for Text Classification

https://github.com/kidist-amde/ddro

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/kidist-amde/ddro-direct-document-relevance-optimization

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/kiko441500/google_bert

Semi-supervised Sequence Learning

https://github.com/kimjeongsun/specaugment

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/king-menin/slavic-ner

Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF

https://github.com/kingcheng2000/bert

Semi-supervised Sequence Learning

https://github.com/kingoflolz/mesh-transformer-jax

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

https://github.com/kinimod23/NMT_Project

Deep contextualized word representations

https://github.com/kinit-sk/l3d-sensitivity-investigation

Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance

https://github.com/kit-mrt/red-motion

Longformer: The Long-Document Transformer

https://github.com/kit-mrt/road-barlow-twins

Longformer: The Long-Document Transformer

https://github.com/kkirchheim/pytorch-ood

Pointer Sentinel Mixture Models

https://github.com/klauscc/dam

Zero-Shot Video Question Answering via Frozen Bidirectional Language Models

https://github.com/knnaraghi/fewshot

Matching Networks for One Shot Learning

https://github.com/knotgrass/attention/blob/main/attn/attention.py

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

https://github.com/knowlab/bi-weekly-paper-presentation

Mistral 7B

https://github.com/knuddj1/op_text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/knuddy/op_text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/kohakublueleaf/kgen

TIPO: Text to Image with Text Presampling for Prompt Optimization

https://github.com/kohjingyu/search-agents

Tree Search for Language Model Agents

https://github.com/koustuvsinha/clutrr-workshop

Compositional Language Understanding with Text-based Relational Reasoning

https://github.com/kpe/params-flow

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/kpot/keras-transformer

Universal Transformers

https://github.com/krantirk/BERT-Pretrained-model

Semi-supervised Sequence Learning

https://github.com/ku21fan/cll-str

How multilingual is Multilingual BERT?

https://github.com/kucukemre96/COMP541

Memory-Efficient Adaptive Optimization

https://github.com/kuldeep7688/BioMedicalBertNer

SciBERT: A Pretrained Language Model for Scientific Text

https://github.com/kunde122/bilm-tf

Deep contextualized word representations

https://github.com/ky941122/darts-experiment

DARTS: Differentiable Architecture Search

https://github.com/kyegomez/CM3Leon

Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

https://github.com/kyegomez/GPT3

Language Models are Few-Shot Learners

https://github.com/kyegomez/MMCA

DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention

https://github.com/kyegomez/PALI

PaLI-X: On Scaling up a Multilingual Vision and Language Model

https://github.com/kyegomez/PALI3

PaLI-3 Vision Language Models: Smaller, Faster, Stronger

https://github.com/kyegomez/PALM-E

PaLM-E: An Embodied Multimodal Language Model

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