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AmosMaru/kiswahili-kikuyu

Domain:

natural language processing

Record type:

software
Creator:
Amo
Host:
# Seq2Seq-Machine-Translation ``` git clone github.com cd Seq2Seq-Machine-Translation mkdir trained_model ``` Check DEMO Notebook ## Training the model Place the data in data/ directory. ``` python train.py -h ``` ``` usage: train.py [-h] [--epochs EPOCHS] [--lr LR] optional arguments: -h, --help show this help message and exit --epochs EPOCHS no of epochs to train --lr LR learning rate ``` Change the hyperparameters as needed and optimizer in the train.py file and train the model. Data preprocessing will be done before training. ``` python train.py --epoch=5000 --lr=0.0001 ``` ``` 135842 translation pairs found in dataset. Reduced dataset to 135690 translation pairs. No of words in each language: eng 12996 fra 21267 Starting Training Loop... 3m 44s (- 0m 0s) (5000 100%) 5.0887 ``` ## Translationg English sentence to French with the trained model. make sure the saved model exists in trained_model/ directory/ ``` python translate.py -h ``` ``` usage: translate.py [-h] [--translate_sentence TRANSLATE_SENTENCE] optional arguments: -h, --help show this help message and exit --translate_sentence TRANSLATE_SENTENCE sentence to translate ``` ``` !python translate.py --translate_sentence='This is my sister!' ``` ``` 135842 translation pairs found in dataset. Reduced dataset to 135690 translation pairs. No of words in each language: eng 12996 fra 21267 input = this is my sister ! output = je ne que vous vous pas ```

Visit

github.com

Tasks

machine translation

Languages

GikuyuSwahiliSwahili, CoastalSwahili, Congo

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