Optimization of Unsupervised Neural Machine Translation using Python
Title:- Unsupervised Byte Pair Encoding for Low-Resource Language Neural Machine Translation
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Implementation Plan:
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Step 1: Initially, we collect and load the Dataset.
Step 2: Then, we preprocess the data using Byte-Pair Encoding.
Step 3: Next, we implement the Neural Machine Translation model.
Step 4: Then, we train and test the model with the collected dataset.
Step 5: Finally, we plot graph for the following metrics:
5.1: Accuracy (%) vs Number of Epochs
5.2: Precision (%) vs Number of Epochs
5.3: F1-Score (%) vs Number of Epochs
5.4: Recall (%) vs Number of Epochs
5.5: Bleu Score vs Number of Epochs
5.6: SacreBlue Score vs Number of Epochs
Software Requirement:
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1. Development Tool: Python – 3.11.4
2. Operating System: Windows 10 (64-bit)
Dataset:
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https://objectstore.e2enetworks.net/a...
Note:
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the above plan satisfies your requirement please confirm with us.
4) We develop simulation based projects only, not in real time.
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#NeuralMachineTranslation
#UnsupervisedTranslation
#PythonProgramming
#MachineLearning
#AI
#NLP
#DataScience
#DeepLearning
#OptimizationTechniques
#languageprocessing
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