[Intensive reading of NLP classic papers] Improving Language Understanding by Generative Pre-Training - Code World

[Intensive reading of NLP classic papers] Improving Language Understanding by Generative Pre-Training

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[Intensive reading of NLP classic papers] Improving Language Understanding by Generative Pre-Training

[NLP classic paper intensive reading] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

[Intensive reading of NLP classic papers] Language Models are Unsupervised Multitask Learners

【论文笔记】GPT-1：Improving Language Understanding by Generative Pre-Training

GPT1解读：Improving Language Understanding by Generative Pre-Training

Re45：读论文 GPT-1 Improving Language Understanding by Generative Pre-Training

[Intensive reading of NLP classic papers] Language Models are Few-Shot Learners

Intensive reading of Li Mu's paper: BERT "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"

[NLP classic paper intensive reading] LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS

[NLP classic paper intensive reading] Attention Is All You Need

Intensive Reading of SIMBAR Papers

Intensive reading of GAN papers

Intensive Reading of Transformer Papers

[Intensive reading of papers] HugNLP: A Unified and Comprehensive Library for Natural Language Processing

[Notes] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Intensive Reading of YOLO Series Papers

Super detailed interpretation of classic neural network papers (6) - DenseNet study notes (translation + intensive reading + code reproduction)

In-depth understanding of deep learning - BERT derived model: SpanBERT (Improving Pre-training by Representing and Predicting Spans)

[NLP classic paper intensive reading] Prefix-Tuning: Optimizing Continuous Prompts for Generation

【Intensive reading of papers】MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

[Intensive reading of papers] GPT-NER: Named Entity Recognition via Large Language Models

NLP practice - use your own corpus for Mask Language Model pre-training

[NLP] 1. BERT | Two-way transformer pre-training language model

[Natural Language Processing | BERT] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Paper Explanation

【论文笔记】BLIP: Bootstrapping Language-Image Pre-training forUnified Vision-Language Understanding and

Resource inventory丨9 classic vision-language multimodal pre-training datasets

Super detailed interpretation of classic neural network papers (5) - ResNet (residual network) study notes (translation + intensive reading + code reproduction)

Intensive reading of deep learning papers [8]: ParseNet

Intensive reading of deep learning papers [7]: nnUNet

[Intensive reading of papers] Fast R-CNN

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