Papers with Code 아카이브

Sentence

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/codehacpj/Image_description

Show and Tell: A Neural Image Caption Generator

https://github.com/codelion/pts

Thought Anchors: Which LLM Reasoning Steps Matter?

https://github.com/coderalo/QACNN-PyTorch

Query-based Attention CNN for Text Similarity Map

https://github.com/codykala/ABCNN

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

https://github.com/coldmist-lu/mqm_ape

xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection

https://github.com/coleridge72/quick_thoughts

An efficient framework for learning sentence representations

https://github.com/common-english/bert-all

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/congvm-it/pyciderevalcap

CIDEr: Consensus-based Image Description Evaluation

https://github.com/congyingxia/Multi-Grained-NER

Multi-Grained Named Entity Recognition

https://github.com/contemn1/sentence_embeddings

Universal Sentence Encoder

https://github.com/context-mover/context-mover-distance-and-barycenters

Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations

https://github.com/contextlab/abstract2paper

Language Models are Few-Shot Learners

https://github.com/copenlu/check-worthiness-pu-learning

Claim Check-Worthiness Detection as Positive Unlabelled Learning

https://github.com/cosmoquester/seq2seq

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/cove-adml/adml-anon

Convolutional Neural Networks for Sentence Classification

https://github.com/cqcl/lambeq

Something Old, Something New: Grammar-based CCG Parsing with Transformer Models

https://github.com/crazydigger/Callibration-of-GPT

Language Models are Few-Shot Learners

https://github.com/crestonbunch/tbcnn

Convolutional Neural Networks over Tree Structures for Programming Language Processing

https://github.com/croitorualin/reverse-stable-diffusion

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/csitfun/logiqa2.0

Transforming Question Answering Datasets Into Natural Language Inference Datasets

https://github.com/csong27/collision-bert

Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring

https://github.com/ctr4si/attend2u

Attend to You: Personalized Image Captioning with Context Sequence Memory Networks

https://github.com/cui0523/Code6/tree/main/albert

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/cui0523/Code6/tree/main/gnmt_v2

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/cui0523/Code6/tree/main/mass

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/cvlab-columbia/globetrotter

Globetrotter: Connecting Languages by Connecting Images

https://github.com/cypressd1999/FYP_2021

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/cyr19/reproducibility

BERTScore: Evaluating Text Generation with BERT

https://github.com/d-ailin/blind-faith-in-text

Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding

https://github.com/dalek7/attention

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/dalmia/Quora-Question-Pairs

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/dangwalp/qg

Learning to Ask: Neural Question Generation for Reading Comprehension

https://github.com/daniel-hain/ML_course_maastricht

PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT

https://github.com/danielajisafe/Image-Captioning-Model

Show and Tell: A Neural Image Caption Generator

https://github.com/danieldeutsch/bert_score_content_analysis

BERTScore: Evaluating Text Generation with BERT

https://github.com/danielinux7/Multilingual-Parallel-Corpus

On the Impact of Various Types of Noise on Neural Machine Translation

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