/industry-wired/media/agency_attachments/2024/12/04/2024-12-04t130344212z-iw-new.png)
/industry-wired/media/agency_attachments/2024/12/04/2024-12-04t130332454z-iw-new.jpg)
/industry-wired/media/media_files/2025/01/08/JPyRaNZeCHUbcywcK339.jpg)
/industry-wired/media/media_files/2025/01/08/JPyRaNZeCHUbcywcK339.jpg)
Top Pre-Trained AI Models For Developers in 2025
Pre-trained models are deep learning models that have already been trained on many datasets. This offers a strong base for a specific task without starting from scratch. Pre-trained models have changed natural language processing by helping developers in various programming languages. In NLP, they excel at tasks like coding, translation, sentiment analysis, and many more.
BERT
BERT (Bidirectional Encoder Representations from Transformers) is one of the most powerful language models that have been developed by Google. BERT reads the text in both directions simultaneously, making it a good tool for understanding the full content of the sentences. BERT is mainly used for tasks like language translation, sentiment analysis, and text summarisation. Developers can fine-tune BERT for specific applications without the requirement of any huge datasets. Also, BERT allows easy adaptability for a wide range of projects as developers can integrate BERT into their projects by using Hugging Face Transformers, Google TensorFlow, and PyTorch.
RoBERTa
Roberta (Robustly Optimised BERT) is an enhanced version of the BERT created by Facebook AI which understands the language model more effectively. It is trained on a large dataset and uses advanced techniques for powerful tasks like language translation, sentiment analysis, and text summarization. Roberta processes the texts bi-directionally to improve the understanding of sentence meaning. For developers, Roberta is a much more valuable tool as it offers an amazing performance in Natural Language Processing tasks. Instead of developers trying to create a new model from the beginning, Roberta can be fine-tuned to fit any particular task by saving a lot of time and resources. Its integration in libraries like Hugging Face Transformers makes it beneficial for the developers to add their projects and build better and smarter language-based applications like chatbots, voice assistants, etc more quickly and accurately.
ELMo
ELMo (Embeddings from Language Models) is a model that helps computers understand words in sentences better by considering the full context of the sentence. Unlike the old models, ELMo looks at the entire sentence to find out the real meaning of a word. For example- the word “bank” will have different meanings in “river bank” as well as a “bank account”. This makes it better at tasks like understanding sentences, answering questions, or translating texts.
Programmers can use ELMo as it can be fine-tuned to work for any particular task by saving time in building a model from the beginning. It also helps to improve the performance of applications like chatbots, language translation tools, and voice assistants by understanding and identifying the full meaning of a particular sentence more accurately.
Conclusion
The pre-trained models have proven to be useful by developing the space of natural language processing by providing powerful, ready-to-use tools for developers as these models can save time and resources by allowing the developers to fine-tune them for a particular task without the need for extensive training.