https://github.com/rohan-chaudhury/humane-speech-synthesis-through-zero-shot-emotion-and-disfluency-generation
공식 코드Humane Speech Synthesis through Zero-Shot Emotion and Disfluency Generation
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Humane Speech Synthesis through Zero-Shot Emotion and Disfluency Generation
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Automating Code-Related Tasks Through Transformers: The Impact of Pre-training
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Unlocking Efficiency: Adaptive Masking for Gene Transformer Models
GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning
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Segatron: Segment-Aware Transformer for Language Modeling and Understanding
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Importance Weighting Can Help Large Language Models Self-Improve
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
User Behavior Simulation with Large Language Model based Agents
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment
Improving Conversational Recommendation Systems via Counterfactual Data Simulation
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.