BERT.

Bidirectional Encoder Representations from Transformers, a pre-trained language model for NLP tasks.

In-depth explanation

BERT (2018) revolutionized NLP by pre-training a transformer encoder on masked language modeling and next sentence prediction. Unlike previous models that read text left-to-right, BERT reads bidirectionally, capturing context from both directions. BERT is fine-tuned for downstream tasks like classification, QA, and NER, achieving state-of-the-art results.

Examples

EX. 01

BERT-base (110M params)

EX. 02

BERT-large (340M params)

EX. 03

RoBERTa

EX. 04

ALBERT

More in Natural Language Processing

01Named Entity Recognition (NER)Identifying and classifying named entities in text into categories like person, organization, location.02Natural Language Processing (NLP)The field of AI focused on enabling computers to understand, interpret, and generate human language.03Sentiment AnalysisDetermining the emotional tone or opinion expressed in text, typically positive, negative, or neutral.04TokenizationBreaking text into smaller units (tokens) such as words, subwords, or characters.05Word EmbeddingDense vector representations of words that capture semantic meaning and relationships.

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