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/eth-sri/tableak

LAMP: Extracting Text from Gradients with Language Model Priors

https://github.com/ethanbar11/ssm_2d

Mega: Moving Average Equipped Gated Attention

https://github.com/ethanjperez/true_few_shot

Language Models are Few-Shot Learners

https://github.com/ethz-asl/pvlff

Neural Implicit Vision-Language Feature Fields

https://github.com/etna-team/etna

Chronos: Learning the Language of Time Series

https://github.com/eva-giboulot/watermax

A Watermark for Large Language Models

https://github.com/evaportelance/structure-meaning-learning

Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models

https://github.com/evoagentx/evoagentx

Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs

https://github.com/exists-forall/striped_attention

Ring Attention with Blockwise Transformers for Near-Infinite Context

https://github.com/expertailab/spaceqa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/exploration-lab/iitk-semeval-2024-task-1

TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

https://github.com/expz/annotated-hyena

Hyena Hierarchy: Towards Larger Convolutional Language Models

https://github.com/f1yfisher/drivedreamer2

DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

https://github.com/fabianwinter93/JAX/tree/main/GateLoop

GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling

https://github.com/fabio-deep/ReZero-ResNet

ReZero is All You Need: Fast Convergence at Large Depth

https://github.com/facebook/MazeBase

End-To-End Memory Networks

https://github.com/facebook/bAbI-tasks

End-To-End Memory Networks

https://github.com/facebookarchive/MazeBase

End-To-End Memory Networks

https://github.com/facebookarchive/SCRNNs

Learning Longer Memory in Recurrent Neural Networks

https://github.com/facebookarchive/babi-tasks

End-To-End Memory Networks

https://github.com/facebookresearch/CodeGen

DOBF: A Deobfuscation Pre-Training Objective for Programming Languages

https://github.com/facebookresearch/DisCo

Mask-Predict: Parallel Decoding of Conditional Masked Language Models

https://github.com/facebookresearch/EgoTV

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

https://github.com/facebookresearch/MLQA

Cross-lingual Language Model Pretraining

https://github.com/facebookresearch/ParlAI

Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion

https://github.com/facebookresearch/UnsupervisedMT

Cross-lingual Language Model Pretraining

https://github.com/facebookresearch/anli

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/facebookresearch/audiocraft

Simple and Controllable Music Generation

https://github.com/facebookresearch/code-prediction-transformer

Character-Level Language Modeling with Deeper Self-Attention

https://github.com/facebookresearch/data2vec_vision

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/facebookresearch/doc-storygen-v2

OpenAssistant Conversations -- Democratizing Large Language Model Alignment

https://github.com/facebookresearch/fairscale

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

https://github.com/facebookresearch/fairseq

Sequence-to-Sequence Learning as Beam-Search Optimization

https://github.com/facebookresearch/fairseq/tree/ust/examples/w2vbert

W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

https://github.com/facebookresearch/fairseq2

Mistral 7B

https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/model.py

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

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