New Words Prediction

October 2023 – December 2023

NLP Deep Learning LSTM Transformers Python

Overview

An advanced Natural Language Processing system that predicts the next word in a sequence using deep learning techniques. The model is trained on large text corpora and can generate contextually relevant word suggestions for autocomplete and text generation applications.

Key Features

  • Context-Aware Predictions: Utilizes contextual information from preceding words to generate accurate predictions.
  • Multiple Model Architectures: Implemented and compared LSTM, GRU, and Transformer-based models.
  • Real-time Inference: Optimized model serving for sub-50ms response times.
  • Multi-domain Support: Trained on diverse text domains including technical documentation, literature, and conversational data.
  • Top-K Predictions: Generates multiple word suggestions ranked by probability.
  • Interactive Demo: Web-based interface for testing predictions in real-time.

Technical Implementation

  • Data Collection & Processing:
    • Curated dataset from multiple sources including books, articles, and conversational data
    • Preprocessed 50M+ words with tokenization, lemmatization, and cleaning
    • Implemented efficient data pipelines using TensorFlow data API
  • Model Architecture:
    • Developed LSTM-based sequence model with 3 layers and 512 hidden units
    • Implemented attention mechanism for improved context understanding
    • Experimented with pre-trained transformers (GPT-2, BERT) for transfer learning
    • Used word embeddings (Word2Vec, GloVe) for semantic representation
  • Training & Optimization:
    • Applied teacher forcing and scheduled sampling techniques
    • Implemented gradient clipping and dropout for regularization
    • Optimized hyperparameters using grid search and Bayesian optimization
    • Achieved 65% top-1 accuracy and 85% top-5 accuracy on test set
  • Deployment:
    • Quantized model for faster inference (TensorFlow Lite)
    • Built REST API using Flask for model serving
    • Implemented caching strategy for frequently predicted sequences

Results & Metrics

  • Achieved 65% top-1 prediction accuracy on diverse test corpus.
  • Reached 85% top-5 accuracy for word suggestions.
  • Reduced model size by 4x through quantization while maintaining 95% accuracy.
  • Average inference time: 35ms per prediction.
  • Perplexity score: 42.5 on validation set.

Applications

  • Smart keyboard autocomplete
  • Text generation and content creation
  • Search query suggestions
  • Email and message composition assistance
  • Code completion for programming environments

Technologies Used

Deep Learning: TensorFlow, Keras, PyTorch
NLP: NLTK, SpaCy, Transformers (Hugging Face)
Embeddings: Word2Vec, GloVe, FastText
API: Flask, RESTful design
Frontend: HTML, CSS, JavaScript
Tools: Jupyter Notebook, TensorBoard, Weights & Biases

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