What is a pre-trained model?

A pre-trained model, having been trained on extensive data, serves as a foundational model for various tasks, leveraging its learned patterns and features. In natural language processing (NLP), these models are commonly employed as a starting point for tasks like language translation, sentiment analysis, and text summarization. Utilizing pre-trained models allows NLP practitioners to economize on time and resources, bypassing the need to train a model from scratch on a large dataset.

Some popular pre-trained models for NLP include BERT, GPT-2, ELMo, and RoBERTa. These models are trained on large datasets of text and can be fine-tuned for specific tasks.

Why do we use Pretrained Models?

Pretrained models are effective and efficient solutions for developers, researchers and businesses as they eliminate the need to write and train the code from scratch, saving time. Other advantages of using pre-trained models for projects are:

• Reduces the computational burden required for initial model training hence, making development more accessible.
• The learned knowledge can be used for various applications.
• Models can be fine-tuned according to the task and can result in superior performance to training from the initial point.
• Less labelled data is required for fine-tuning specific tasks.

Applications of pre-trained Models in NLP

• Language translation: NLP algorithms facilitate the automatic translation of text or speech, promoting communication across language barriers.
• Sentiment analysis: NLP algorithms enable automated analysis of text or speech to discern sentiment (positive, negative, or neutral), beneficial for tasks like customer feedback assessment and social media surveillance.
• Chatbot development: NLP algorithms can be used to develop chatbots that can understand and respond to human language. This is useful for applications such as customer service, where chatbots can handle routine inquiries and free up human agents to handle more complex tasks.
• Text summarization: NLP algorithms can be used to automatically summarize long documents or articles by extracting the most important information and presenting it in a condensed form. This is useful for applications such as news aggregation and information extraction.
• Sentence completion: NLP algorithms can automatically generate sentence or paragraph continuations by considering the context and content of the entered text.

The application of pretrained models is not limited to NLP, it is also used for image classification, image segmentation and other computer vision applications.

Pretrained models are deep learning models that have been trained on huge amounts of data before fine-tuning for a specific task. The pre-trained models have revolutionized the landscape of natural language processing as they allow the developer to transfer the learned knowledge to specific tasks, even if the tasks differ from the original training data. The article aims to provide a comprehensive knowledge of pre-trained models that are the driving force behind smart NLP-basedthe AI models like ChatGPT, Gemini, Bard and more.