Natural Language Processing Services

BERT (Bidirectional Encoder Representations from Transformers) is an encoder-only model it reads text bidirectionally and produces dense representations for each token. It excels at classification, NER, and extractive QA tasks where understanding the full context of a text matters. GPT models are decoder-only they generate text autoregressively (token by token). They excel at generation tasks (summarisation, translation, creative writing). For classification and extraction NLP tasks in production, fine-tuned BERT variants (RoBERTa, DeBERTa, ALBERT) are typically preferred over using a large GPT model via API they are smaller (faster, cheaper inference), can be self-hosted (no data leaving your environment), and achieve comparable or better accuracy on classification tasks when properly fine-tuned.

Token-level classification for NER uses BIOES (Begin, Inside, Outside, End, Single) tagging scheme each token is labelled with its entity type and position. For a three-word product name "Apple AirPods Pro": B-PRODUCT (Apple), I-PRODUCT (AirPods), E-PRODUCT (Pro). Single-token entities get S-ENTITY tag. This precise boundary detection enables extraction of multi-token entities and correct handling of adjacent entities of different types. ClickMasters uses BIOES tagging for all production NER models.