Skip to content

Next Word Prediction using LSTM

AIM

To predict the next word using LSTM.

Dataset

Code

LIBRARIES NEEDED

LIBRARIES USED
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
  • seaborn
  • tensorflow
  • keras

DESCRIPTION

What is the requirement of the project?

  • To create an intelligent system capable of predicting the next word in a sentence based on its context.
  • The need for such a system arises in applications like autocomplete, chatbots, and virtual assistants.
Why is it necessary?
  • Enhances user experience in text-based applications by offering accurate suggestions.
  • Reduces typing effort, especially in mobile applications.
How is it beneficial and used?
  • Improves productivity: By predicting words, users can complete sentences faster.
  • Supports accessibility: Assists individuals with disabilities in typing.
  • Boosts efficiency: Helps in real-time text generation in NLP applications like chatbots and email composition.
How did you start approaching this project? (Initial thoughts and planning)
  • Studied LSTM architecture and its suitability for sequential data.
  • Explored similar projects and research papers to understand data preprocessing techniques.
  • Experimented with tokenization, padding, and sequence generation for the dataset.
Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.).
  • Blogs on LSTM from Towards Data Science.
  • TensorFlow and Keras official documentation.

EXPLANATION

DETAILS OF THE DIFFERENT FEATURES

PROJECT WORKFLOW

Initial data exploration and understanding:

  • Gathered text data from open-source datasets.
  • Analyzed the structure of the data.
  • Performed basic text statistics to understand word frequency and distribution.

Data cleaning and preprocessing

  • Removed punctuation and convert text to lowercase.
  • Tokenized text into sequences and pad them to uniform length.

Feature engineering and selection

  • Created input-output pairs for next-word prediction using sliding window techniques on tokenized sequences.

Model training and evaluation:

  • Used an embedding layer to represent words in a dense vector space.
  • Implemented LSTM-based sequential models to learn context and dependencies in text.
  • Experimented with hyperparameters like sequence length, LSTM units, learning rate, and batch size.

Model optimization and fine-tuning

  • Adjusted hyperparameters like embedding size, LSTM units, and learning rate.

Validation and testing

  • Used metrics like accuracy and perplexity to assess prediction quality.
  • Validated the model on unseen data to test generalization.

PROJECT TRADE-OFFS AND SOLUTIONS

Accuracy vs Training Time:

  • Solution: Balanced by reducing the model's complexity and using an efficient optimizer.

Model complexity vs. Overfitting:

  • Solution: Implemented dropout layers and monitored validation loss during training.

SCREENSHOTS

Project workflow

  graph LR
    A[Start] --> B{Data Preprocessed?};
    B -->|No| C[Clean and Tokenize];
    C --> D[Create Sequences];
    D --> B;
    B -->|Yes| E[Model Designed?];
    E -->|No| F[Build LSTM/Transformer];
    F --> E;
    E -->|Yes| G[Train Model];
    G --> H{Performant?};
    H -->|No| I[Optimize Hyperparameters];
    I --> G;
    H -->|Yes| J[Deploy Model];
    J --> K[End];

MODELS USED AND THEIR EVALUATION METRICS

Model Accuracy MSE R2 Score
LSTM 72% - -

MODELS COMPARISON GRAPHS

Models Comparison Graphs

model perf

CONCLUSION

KEY LEARNINGS

Insights gained from the data

  • The importance of preprocessing for NLP tasks.
  • How padding and embeddings improve the model’s ability to generalize.
Improvements in understanding machine learning concepts
  • Learned how LSTMs handle sequential dependencies.
  • Understood the role of softmax activation in predicting word probabilities.
Challenges faced and how they were overcome
  • Challenge: Large vocabulary size causing high memory usage.
  • Solution: Limited vocabulary to the top frequent words.

USE CASES

Text Autocompletion

  • Used in applications like Gmail and search engines to enhance typing speed.

Virtual Assistants

  • Enables better conversational capabilities in chatbots and AI assistants.