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predict-next-word

Documentation / train/predict-next-word

Transformer​

Transformer: any;

trainNextWordPrediction()​

function trainNextWordPrediction(): Promise<Object>;

Defined in: train/predict-next-word.js:88

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Predict Next Word Based On Context and Learned Patterns in Training Examples​

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Comprehensive training function for a self-attention transformer using custom torch.js. This function implements a decoder-only transformer architecture similar to GPT models, training it with synthetic data using the Adam optimizer and GPU.js for acceleration. In real applications, this would be:

  • Tokenized text data (e.g., using BPE, WordPiece, or SentencePiece)
  • Loaded from datasets like WikiText, BookCorpus, or Common Crawl
  • Preprocessed with appropriate padding and attention masks

Key architectural components:

  • Token and positional embeddings
  • Multi-head self-attention blocks
  • Layer normalization
  • Linear output projection
  • Cross-entropy loss computation
  • Adam optimization with backpropagation

Advanced usage scenarios:

  • Fine-tuning on domain-specific data
  • Transfer learning from pre-trained weights
  • Ensemble methods with multiple trained models
  • Model compression and quantization

Returns​

Promise<Object>

Training results containing final loss and model

Example​

import { trainNextWordPrediction, Transformer } from './transformer-training.js';

// Train a new model
const results = await trainNextWordPrediction();
console.log('Training completed with final loss:', results.finalLoss);

// Generate a language response based using the trained model
const model = results.model;
const predictions = model.forward(inputTokens);

Author​

ai-research-agent

See​