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Language Modelling

514 평가 기록 88 datasets

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
RETRO (7.5B)
Test perplexity: 2.4Number of params: 7532M
논문 코드
GPT-3 (Zero-Shot)
Test perplexity: 20.5Params: 175000M
논문 코드
GPT-2 (48 layers, h=1600)
Bit per Character (BPC): 0.93Number of params: 1542M
논문 코드
Test-Time Fine-Tuning with SIFT + Llama-3.2 (3B)
Bits per byte: 0.557
논문 코드
SparseGPT (175B, 50% Sparsity)
Test perplexity: 8.21
논문 코드
PaLM-540B (Few-Shot)
Accuracy: 89.7
논문 코드
MDLM (AR baseline)
PPL: 20.09Number of params: 110M
논문 코드
GPT-2
Bit per Character (BPC): 0.98Number of params: 1542M
논문 코드
Mogrifier LSTM + dynamic eval
Bit per Character (BPC): 1.083Number of params: 24M
논문 코드
Transformer-XL + RMS dynamic eval
Bit per Character (BPC): 0.94Number of params: 277M
논문 코드
CoLT5 XL
GovRep: 61.3/32.2/33.8SumScr: 36.4/10.2/21.7QMSum: 36.2/12.9/24.3Qspr: 53.9
논문 -
MDLM-Prime
parameters: 131Meval_perplexity: 15.36
논문 -

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논문

Mogrifier LSTM

1909.01792 2019-09-04 이 페이지의 평가 1건
Validation perplexity: 44.8Test perplexity: 44.9Params: 24M

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