Text Generation
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Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pwc-1/Paper-9/tree/main/2/mbart50
IndicBART: A Pre-trained Model for Indic Natural Language Generation
https://github.com/pwc-1/Paper-9/tree/main/2/mvp
MVP: Multi-task Supervised Pre-training for Natural Language Generation
https://github.com/pwc-1/Paper-9/tree/main/4/Translation-Invariant/model
https://github.com/pwc-1/Paper-9/tree/main/longformer
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
https://github.com/pwc-1/Paper-9/tree/main/longt5
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/qfzhu/st
https://github.com/qingyun-wu/autogen-eval
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
https://github.com/qq345736500/sarcasm
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/qwenlm/qwen1.5
https://github.com/qwenlm/qwen2
https://github.com/r-three/RAD
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
https://github.com/rabiulcste/vqazero
COSMic: A Coherence-Aware Generation Metric for Image Descriptions
https://github.com/rachit-shah/News-Classfication-using-DNN-models
A Hierarchical Neural Autoencoder for Paragraphs and Documents
https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch
https://github.com/radzim/obfuscated
Measuring Mathematical Problem Solving With the MATH Dataset
https://github.com/rah4927/lean-dojo-mew
Measuring Mathematical Problem Solving With the MATH Dataset
https://github.com/rahul96rajan/text-2-strokes
https://github.com/rahulbhalley/Recurrent-Nets-in-Pytorch
https://github.com/rahulbhalley/rnns-in-pytorch
https://github.com/rajlm10/Chandler
https://github.com/ramanakshay/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ramanakshay/nanogpt
https://github.com/ramanakshay/transformer
https://github.com/ranggihwang/pregated_moe
https://github.com/rasbot/plot-bot
https://github.com/ratishsp/data2text-1
https://github.com/ratishsp/mlb-data-scripts
https://github.com/raufer/bert-summarization
Pretraining-Based Natural Language Generation for Text Summarization
https://github.com/raymondhs/char-rnn-truecase
https://github.com/raymondhs/fairseq-extensible-mbart
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/rdevon/BGAN
https://github.com/re-search/DocProduct
https://github.com/reactive-systems/circuit-repair
https://github.com/reactive-systems/ml2
https://github.com/reallygooday/60daysofudacity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.