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/sy-sunmoon/Clever-Commenter-Let-s-Try-More-Apps

End-To-End Memory Networks

https://github.com/syyunn/node2vec

DeepWalk: Online Learning of Social Representations

https://github.com/t080/pytorch-translm

Sample Efficient Text Summarization Using a Single Pre-Trained Transformer

https://github.com/taesunwhang/BERT-ResSel

An Effective Domain Adaptive Post-Training Method for BERT in Response Selection

https://github.com/tagoyal/factuality-datasets

On Faithfulness and Factuality in Abstractive Summarization

https://github.com/takuseno/d3rlpy

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/tally0818/DARTS

DARTS: Differentiable Architecture Search

https://github.com/tally0818/NASNet

Neural Architecture Search with Reinforcement Learning

https://github.com/tamsiuhin/oppu

LaMP: When Large Language Models Meet Personalization

https://github.com/tanmaylaud/Patient_Conversation_Classifier_FastAI

Universal Language Model Fine-tuning for Text Classification

https://github.com/tanny411/llm-reliability-and-consistency-evaluation

TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability

https://github.com/tanvir-ishraq/healifyai--llm-based-healthcare-system

Universal Language Model Fine-tuning for Text Classification

https://github.com/tanyuqian/cappy

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

https://github.com/taokz/biomedgpt

Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning

https://github.com/taoyds/grappa

GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing

https://github.com/taozitongxue1/11bee-DeepSpeech

First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs

https://github.com/tbachlechner/ReZero-examples

ReZero is All You Need: Fast Convergence at Large Depth

https://github.com/tchebonenko/Automated-Topic_Modeling-and-NER

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

https://github.com/tcnguyen/bert

Semi-supervised Sequence Learning

https://github.com/team-tud/ctab-gan

TabuLa: Harnessing Language Models for Tabular Data Synthesis

https://github.com/team-tud/ctab-gan-plus

TabuLa: Harnessing Language Models for Tabular Data Synthesis

https://github.com/teddykoker/mpnn-for-quantum-chem

Order Matters: Sequence to sequence for sets

https://github.com/teganmaharaj/movieFIB

A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering

https://github.com/tempra28/nmtrnng

Recurrent Neural Network Grammars

https://github.com/tensorflow/federated

Federated Learning for Mobile Keyboard Prediction

https://github.com/tensorflow/models/blob/master/official/nlp/tasks/electra_task.py

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

https://github.com/tensorflow/models/tree/master/official/nlp/modeling/layers

Talking-Heads Attention

https://github.com/tensorflow/models/tree/master/research/adversarial_text

Semi-supervised Sequence Learning

https://github.com/tensorflow/models/tree/master/research/lm_1b

One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling

https://github.com/terrierteam/pyterrier_pisa

From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective

https://github.com/teticio/llama-squad

Prefix-Tuning: Optimizing Continuous Prompts for Generation

https://github.com/tetsu9923/scireviewgen

SciBERT: A Pretrained Language Model for Scientific Text

https://github.com/text-machine-lab/extending_psycholinguistic_dataset

What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models

https://github.com/tginart/sglang

SGLang: Efficient Execution of Structured Language Model Programs

https://github.com/thanhlong1997/bert_quora

Semi-supervised Sequence Learning

https://github.com/the-Quert/iNLPfun

Semi-supervised Sequence Learning

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