RNN + char2-MS-vec

Parameters

158M

Published

Jun 13, 2019

RNN + char2-MS-vec is an AI model developed by NTT Communication Science Laboratories and Tohoku University (Japan), first published in June 2019. It works in the language domain, on tasks such as language modeling.

Epoch AI has no training-compute estimate for this model. The model has 158,000,000 parameters. Training ran on NVIDIA Tesla P100 PCIe 16GB.

Access: Unreleased. Its weights are not openly released. Epoch AI rates the confidence of this record as confident.

Full record

Organization: NTT Communication Science Laboratories, Tohoku University
Country of organization: Japan
Domain: Language
Task: Language modeling
Parameters: 158,000,000
Training hardware: NVIDIA Tesla P100 PCIe 16GB
Model accessibility: Unreleased
Open weights: No
Epoch confidence: Confident

More from NTT Communication Science Laboratories,Tohoku University

(ensemble): AWD-LSTM-DOC (fin) × 5 (WT2)AWD-LSTM-DOC (fin) (23M)RNN + char4-MS-vec RNN + char3-MS-vec (ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)AWD-LSTM-DOC (fin) (37M)

SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.

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