Natural Language Processing Tutorial

Build state-of-the-art language models with AI Dino's NLP toolkit

Getting Started

Installation

Install our core NLP library with all required dependencies 

npm install @ai-dino/nlp

First Model

Load a pre-trained transformer-based language model 

from ai_dino.nlp import LanguageModel

model = LanguageModel("bert-base-icv")

Step-by-Step Implementation

1. Model Configuration

// Choose a pre-trained language model
const model = new LanguageModel({
  modelType: 'roberta-base',
  max_length: 512,
  pretrained: true
});

Available model types: bert-base, gpt-2, roberta, distilbert, and custom configurations.

2. Training Your Model

const dataset = new TextDataset('path/to/text-files', {
  batch_size: 16,
  max_seq_length: 128
});

await model.train(dataset, {
  epochs: 3,
  learning_rate: 3e-4
});

Our system includes full training pipelines with progress tracking and model checkpointing.

3. Making Predictions

const results = model.predict([
  "This technology is amazing!"
]);

console.log(results); 
// Returns sentiment scores and contextual embeddings

The model can handle text classification, entity recognition, and full-text analysis.

4. Performance Optimization

Speed Tips
  • Use mixed precision training
  • Leverage GPU acceleration
  • Enable quantization for inference
Accuracy Enhancements
  • Apply domain-specific fine-tuning
  • Use adversarial training samples
  • Implement model ensembling

Best Practices

Data Preparation

  • Use tokenization-aware text normalization
  • Handle OOV tokens with vocabulary expansion
  • Balance training data distributions

Training

  • Monitor perplexity metric tracking
  • Use learning rate warmup scheduling
  • Track attention weight distributions

Evaluation

  • Compute ROUGE similarity scores
  • Measure model uncertainty estimates
  • Track attention pattern coherence

Need Expert Guidance?

Our NLP engineers can help you design optimal language processing pipelines

Contact Language Experts
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