Building Cutting-Edge Android / iOS Apps with 4 Machine Learning Text Models

In my previous blog post, I primarily discussed leading vision models focusing on both video and image categories. Today, my focus shifts to exploring open-source TensorFlow Lite models specifically tailored for text-related tasks.

Text Models :

Machine learning Text models are algorithms and techniques used to process and analyze textual data. These models are designed to extract meaningful information from text, such as sentiment, topics, or relationships between words. Some common machine learning text models.

These machine learning text models serve different purposes and excel in various natural language processing tasks, including sentiment analysis, text classification, machine translation, named entity recognition, and text generation. Choosing the appropriate model depends on the specific task requirements, available data, and computational resources.

BERT Question and Answer:
Use Case: Sentiment Analysis
Description: BERT is a powerful text representation model that captures contextual relationships in language by pre-training on large text corpora. It’s widely used for various natural language understanding tasks, including sentiment analysis, text classification, and question answering.
Implementation Link: TensorFlow Lite BERT
Sample Applications: BERT has been integrated into various mobile applications for sentiment analysis, chatbots, and virtual assistants.

Smart reply:
Use Case: Text Classification
Description: The smart reply model produces response suggestions by analyzing chat messages. These suggestions aim to provide contextually relevant, single-tap responses, making it easier for users to promptly reply to incoming messages.
Implementation Link: TensorFlow Lite Smart Reply
Sample Applications: According to the documentation this model has been integrated inside Gmail, Allo, Smart Replies on Android Wear inorder to do the smart reply.

Auto Complete:
Use Case: Text Classification
Description: Auto-Complete TensorFlow models are neural language models designed to predict and suggest the completion of text based on the input provided. These models utilize deep learning techniques to generate contextually relevant suggestions for text completion tasks, such as auto-completion of sentences, search queries, or code snippets and current Tensorflow model build on top of Keras GPT-2 this model has been converted to Tensorflow lite with the help of tlite library support whole implementation process including conversion is explained below in the document.
Implementation Link: TensorFlow Lite Auto Completion
Sample Applications: This model can be used in the applications like Search Engine, Text Editors, Messaging Apps, Email Clients, Programming Environment.

Text classification:
Use Case: Text Classification
Description:Text classification involves assigning a paragraph to predefined categories according to its content, enhancing organization and analysis.This pre-trained model accurately determines the sentiment of paragraphs, distinguishing between positive and negative tones. Trained on the extensive Large Movie Review Dataset v1.0 by Mass et al, this model utilizes IMDB movie reviews to predict sentiment.
Implementation Link: TensorFlow Lite Text Classification
Sample Applications: This model can be used in the applications like Sentiment Analysis in Customer Reviews, News Categorization, Spam Detection in Emails, Social Media Content Moderation.

This is not the end of this blog, in my coming blog posts, I will be talking about more Machine Learning models which Mobile Developers (IOS Developers and Android Developers) can make use of in their applications

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