https://huggingface.co/facebook/muppet-roberta-large
공식 코드Muppet: Massive Multi-task Representations with Pre-Finetuning
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Muppet: Massive Multi-task Representations with Pre-Finetuning
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model
ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing
Dict-BERT: Enhancing Language Model Pre-training with Dictionary
MolXPT: Wrapping Molecules with Text for Generative Pre-training
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Synthesizer: Rethinking Self-Attention in Transformer Models
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.