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

https://github.com/hakeematyab/Neural-Image-Caption-Generator-Show-and-Tell-Paper-Replication

Show and Tell: A Neural Image Caption Generator

https://github.com/hamil168/Chatbots

A Neural Conversational Model

https://github.com/hamzah-luqman/arabsign

ArabSign: A Multi-modality Dataset and Benchmark for Continuous Arabic Sign Language Recognition

https://github.com/handdl/btcnn

Convolutional Neural Networks over Tree Structures for Programming Language Processing

https://github.com/hanfeng108/Language-Detection

Convolutional Neural Networks for Sentence Classification

https://github.com/hantek/SelfAttentiveSentEmbed

A Structured Self-attentive Sentence Embedding

https://github.com/hantek/distance-parser

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

https://github.com/haodeqi/BenchmarkingIntentDetection

Efficient Intent Detection with Dual Sentence Encoders

https://github.com/haoshuai999/News-summarization

Content Selection in Deep Learning Models of Summarization

https://github.com/harshithbelagur/Neural-Machine-Translation

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/harvardnlp/decomp-attn

A Decomposable Attention Model for Natural Language Inference

https://github.com/hashi0203/image-captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/hate-alert/HateALERT-EVALITA

Hateminers : Detecting Hate speech against Women

https://github.com/hazelnutsgz/Naive-LSTM-CRF

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/hazyresearch/ama_prompting

Language Models are Few-Shot Learners

https://github.com/hc495/staicc

Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts

https://github.com/heatz123/naturalspeech

NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality

https://github.com/hero9968/lipnet-python

LipNet: End-to-End Sentence-level Lipreading

https://github.com/heylinsir/semantic-based-qa

A Semantic-based Method for Unsupervised Commonsense Question Answering

https://github.com/hhzrd/BEFAQ

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/hi-underworld/Imformation-Retrieval-weekly-report

Convolutional Neural Networks for Sentence Classification

https://github.com/hiddenmaze/text2action

Text2Action: Generative Adversarial Synthesis from Language to Action

https://github.com/hiejulia/data-mining

A Neural Conversational Model

https://github.com/hieudepchai/BERT_IE

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

https://github.com/hilberthit/gpt-3

Language Models are Few-Shot Learners

https://github.com/hiun/learning-transformers

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/hiyouga/PBAN-PyTorch

Effective LSTMs for Target-Dependent Sentiment Classification

https://github.com/hiyouga/llama-efficient-tuning

Long and Diverse Text Generation with Planning-based Hierarchical Variational Model

https://github.com/hiyoung123/SoftMaskedBert

Spelling Error Correction with Soft-Masked BERT

https://github.com/hle027/IR-Competition

SPECTER: Document-level Representation Learning using Citation-informed Transformers

https://github.com/hnt4499/seq2seq

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/hoangcuongnguyen2001/scibert-for-technique-classification

SciBERT: A Pretrained Language Model for Scientific Text

https://github.com/hobincar/RecNet

Reconstruction Network for Video Captioning

https://github.com/hojjat-mokhtarabadi/promptsource

Language Models are Few-Shot Learners

https://github.com/hooman650/supcl-seq

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/horizonheart/ELMO

Evaluation of sentence embeddings in downstream and linguistic probing tasks

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