https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-tiny
공식 코드ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization
ERNIE-Doc: A Retrospective Long-Document Modeling Transformer
Context Perception Parallel Decoder for Scene Text Recognition
StrucTexT: Structured Text Understanding with Multi-Modal Transformers
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
Compositional Entailment Learning for Hyperbolic Vision-Language Models
Enhancing Indic Handwritten Text Recognition Using Global Semantic Information
PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search
Can a Large Language Model Learn Matrix Functions In Context?
PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
Content Planning for Neural Story Generation with Aristotelian Rescoring
Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples
CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification
Ranking and Classification driven Feature Learning for Person Re_identification
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds
Towards a text-based quantitative and explainable histopathology image analysis
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Text Counterfactuals via Latent Optimization and Shapley-Guided Search
CPT: Efficient Deep Neural Network Training via Cyclic Precision
RLLTE: Long-Term Evolution Project of Reinforcement Learning
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content
On Effects of Steering Latent Representation for Large Language Model Unlearning
Facilitating large language model Russian adaptation with Learned Embedding Propagation
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.