PolyTransNet is designed to function in a modular environment, utilizing components from both OLA Krutrim and custom-developed wrappers by DeepQuery. The architecture allows for high flexibility in deployment, with each component capable of scaling independently based on workload requirements. The system is optimized for cloud-based environments, leveraging serverless technologies to reduce operational overhead and improve cost efficiency.
Core Components:
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Translation Engine: Translates text between multiple languages using state-of-the-art neural machine translation (NMT) techniques.
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Embedding Generator: Converts text into dense vector representations that can be utilized for downstream NLP tasks such as search, classification, and recommendation.
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Text Classification Module: A deep learning model trained to classify text into predefined categories based on the content and context of the input text.
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Retrieval Engine: Supports efficient retrieval of relevant information from large datasets by comparing text embeddings, optimized using Krutrim's infrastructure.