https://github.com/wenet-e2e/wenet
공식 코드WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
Keeping it Simple: Language Models can learn Complex Molecular Distributions
Learning to Locate Visual Answer in Video Corpus Using Question
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
TabFact: A Large-scale Dataset for Table-based Fact Verification
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
Bioformer: an efficient transformer language model for biomedical text mining
AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting
AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning
Iterative Few-shot Semantic Segmentation from Image Label Text
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation
Self-Evolution Learning for Discriminative Language Model Pretraining
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
Layer-Condensed KV Cache for Efficient Inference of Large Language Models
Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.