encoder-only models - Langformers

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encoder-only models

A collection of 4 posts

embedding models

Build your own custom, lightweight transformer with Langformers

In the world of machine learning, bigger isn't always better — especially when it comes to deploying models in resource-constrained environments like mobile apps or real-time systems. Fortunately, Langformers offers an elegant solution: you can train a smaller model to mimic the embeddings of a large pretrained model. This

encoder-only models

Sentence Embedding and Similarity with Langformers

When working with semantic search or designing sophisticated NLP pipelines such as RAG (Retrieval-augmented Generation), converting text into numerical vectors—a process called embedding—is often one of the very first steps. Embeddings capture the meaning and context of sentences in a way that machines can understand, enabling powerful applications

encoder-only models

Pretrain Your Own RoBERTa model from Scratch

Masked Language Models (MLMs) like BERT, RoBERTa, and MPNet have revolutionized the way we understand and process language. These models are foundational for tasks such as text classification, named-entity recognition (NER), and many other NLP applications where the entire input sequence matters. But what if you want to create your

encoder-only models

Fine-Tune Hugging Face Models for Text Classification

Training text classifiers has traditionally required a lot of setup, coding, and hyperparameter tuning. But thanks to Langformers, you can now fine-tune powerful Transformer models (encoder-only models / masked lanuage models) like BERT, RoBERTa, or MPNet for your custom classification tasks with just a few lines of code — all while keeping