ParkerAli/N-Gram-Language-Models-for-isiNdebele-with-Feedforward-Neural-Networks

Domain:

natural language processing

Creator:

Par

Host:

In order to train and evaluate any of the models on the isiNdebele datasets, the following must be done: - Create a directory called in "/content/drive/My Drive/" called "NLP A2" - Place the .train, .valid, and .test datasets into "/content/drive/My Drive/NLP A2/" - Mount your Google Colab notebook with your Google Drive - Run each cell sequentially from top to bottom

Tekleab15/N-gram-Language-Models

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georgeedwardd/N-gram-Models

Character-level n-gram models and Byte-Pair Encoding for language identification across Afrikaans, D

Automatic Spelling Corrector for Yorùbá Language Using Edit Distance and N-Gram Language Models

Optimizing n‑gram Order of an n‑gram Based Language Identification Algorithm for 68 Written Languages

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JONAHKYAGABA/N-gram-Language-Model-Construction-for-Oromo-Amharic

N-gram Language Model Construction for Oromo & Amharic # 📚 N-gram Language Model Construction for O

Seasonal rainfall prediction in Juba County, South Sudan using the feedforward neural networks

Historical rainfall data from 1997-2016 of Juba County, South Sudan were used in a Feed-Forward Neur

ParkerAli/N-Gram-Language-Models-for-isiNdebele-with-Feedforward-Neural-Networks

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