https://github.com/satya77/Transformer_Temporal_Tagger
공식 코드BERT got a Date: Introducing Transformers to Temporal Tagging
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
BERT got a Date: Introducing Transformers to Temporal Tagging
UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation
$\textit{LinkPrompt}$: Natural and Universal Adversarial Attacks on Prompt-based Language Models
From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue Agents
Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3
STaR-GATE: Teaching Language Models to Ask Clarifying Questions
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations
Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias
Multi-label Sequential Sentence Classification via Large Language Model
MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks
MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers
MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks
MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts
Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation
Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks
CoPrUS: Consistency Preserving Utterance Synthesis towards more realistic benchmark dialogues
TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.