Natural Language Processing та обробка природної мови

🎓 Transformers

Attention Mechanism

Concept: Attention to different parts of the input.

Self-Attention: Attention within the sequence.

Advantages: Parallelism, long-range dependencies.

BERT

Architecture: Bidirectional encoder.

Pre-training: Masked LM, next sentence prediction.

Fine-tuning: For downstream tasks.

GPT

Architecture: Autoregressive decoder.

Pre-training: Language modeling.

Generation: Text generation.

🔧 NLP Tasks

Text Classification

Tasks: Sentiment, topics, intents.

Models: BERT, fine-tuning.

Applications: Widely used.

Named Entity Recognition

Task: Detecting entities (people, places, organizations).

Models: BERT-based NER.

Applications: Information extraction.

Machine Translation

Task: Translating between languages.

Models: Seq2Seq, Transformers.

Applications: Translation systems.

📚 Practical Examples

Example 1: Sentiment Analysis with BERT

Pre-trained: Download pre-trained BERT.

Fine-tuning: Fine-tune on sentiment data.

Evaluation: Verify accuracy on the test set.

Example 2: Text Generation with GPT

Pre-trained: Use pre-trained GPT.

Generation: Generate text from a prompt.

Fine-tuning: Fine-tune on specific data.

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NLP: Natural Language Processing

Try it live

Everything above runs in your browser — open Dimensionality Reduction: PCA, t-SNE & UMAP and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.