Remove BERT Remove Continuous Learning Remove Natural Language Processing
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Continual Adapter Tuning (CAT): A Parameter-Efficient Machine Learning Framework that Avoids Catastrophic Forgetting and Enables Knowledge Transfer from Learned ASC Tasks to New ASC Tasks

Marktechpost

Continual Learning (CL) poses a significant challenge for ASC models due to Catastrophic Forgetting (CF), wherein learning new tasks leads to a detrimental loss of previously acquired knowledge. These adapters allow BERT to be fine-tuned for specific downstream tasks while retaining most of its pre-trained parameters.

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Training Improved Text Embeddings with Large Language Models

Unite.AI

They serve as a core building block in many natural language processing (NLP) applications today, including information retrieval, question answering, semantic search and more. More recent methods based on pre-trained language models like BERT obtain much better context-aware embeddings.

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Beyond ChatGPT; AI Agent: A New World of Workers

Unite.AI

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Deep learning techniques further enhanced this, enabling sophisticated image and speech recognition.

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Introduction to Large Language Models (LLMs): An Overview of BERT, GPT, and Other Popular Models

John Snow Labs

Are you curious about the groundbreaking advancements in Natural Language Processing (NLP)? Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. and GPT-4, marked a significant advancement in the field of large language models.

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ConfliBERT: A Domain-Specific Language Model for Political Violence Event Detection and Classification

Marktechpost

While domain experts possess the knowledge to interpret these texts accurately, the computational aspects of processing large corpora require expertise in machine learning and natural language processing (NLP).

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Create and fine-tune sentence transformers for enhanced classification accuracy

AWS Machine Learning Blog

Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval.

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LLMs for Chatbots and Conversational AI: Building Engaging User Experiences

Chatbots Life

With deep learning coming into the picture, Large Language Models are now able to produce correct and contextually relevant text even in the face of complex nuances. LLM, with its advanced natural language processing capabilities, can instantly analyze customer queries, understand the context, and generate relevant responses.