What's included

• Fine-tuning LLMs

  Customized fine-tuning of large language models to adapt them for specific domain-related tasks and improve their performance on client-specific use cases.

• Text Classification Models

  Development and deployment of machine learning models for categorizing text data into predefined classes, aiding in automated sorting and organization.

• Text Summarization

  Implementation of algorithms to generate concise summaries from long documents, articles, or datasets, enhancing readability and information extraction.

• Named Entity Recognition (NER)

  Building models to identify and classify key information (names, dates, locations, etc.) within a text, enabling better data structuring and analysis.

• Sentiment Analysis Engines

  Development of tools to analyze and interpret the sentiment or emotional tone behind text data, providing insights into customer opinions and feedback.

• Intent Detection Mechanisms

  Crafting specialized models to detect and interpret user intent behind textual data, crucial for improving user interaction and response accuracy.

• Chatbot Development

  Designing and deploying intelligent chatbots that can understand and respond appropriately to user queries using advanced NLP techniques.