Tools for NLP: Scikit-learn, PyTorch, TensorFlow, and Hugging Face PDF

Introduction to Natural Language Processing Tools for NLP: Scikit-learn, PyTorch, TensorFlow, and Hugging Face Assist. Prof. Dr. Dželila MEHANOVIĆ Why Use Specialized Tools for NLP? NLP challenges: ○ Text preprocessing ○ High-dimensionality of data ○ Sequence modeling...

Introduction to Natural Language Processing Tools for NLP: Scikit-learn, PyTorch, TensorFlow, and Hugging Face Assist. Prof. Dr. Dželila MEHANOVIĆ Why Use Specialized Tools for NLP? NLP challenges: ○ Text preprocessing ○ High-dimensionality of data ○ Sequence modeling ○ Scalability and efficiency Key requirements of tools: ○ Easy-to-use APIs ○ Support for ML/DL models ○ Efficient handling of text data Scikit-learn Overview What is Scikit-learn? ○ A Python library for machine learning. ○ Focused on traditional ML algorithms. Key Features: ○ Preprocessing utilities (e.g., TFIDF, CountVectorizer). ○ Classical algorithms (e.g., SVM, Logistic Regression). ○ Model evaluation tools (e.g., cross-validation). Best for: #install with pip install scikit-learn ○ Small to medium-sized NLP tasks. #verify installation ○ Quick prototyping. import sklearn print(sklearn.__version__) Scikit-learn in NLP Capabilities: ○ Text vectorization: Bag of Words, TFIDF. ○ Feature selection. ○ Non-deep-learning models like Naïve Bayes, SVM, and Random Forest. Limitations: ○ Not designed for deep learning. ○ Limited for large datasets or sequence-based models. TensorFlow Overview What is TensorFlow? ○ Open-source deep learning framework. ○ Developed by Google. Key Features: ○ Static computation graph (eager execution supported). ○ TensorFlow Lite for mobile. ○ Extensive production deployment tools. Best for: #install with pip install tensorflow ○ Production-ready NLP models. #verify installation ○ Scalable solutions for cloud and edge. import tensorflow as tf print(tf.__version__) TensorFlow in NLP Applications: ○ Text classification. ○ Named Entity Recognition NER. ○ TensorFlowʼs Text and Transformers libraries. Popular Libraries: ○ tensorflow_text: Tokenization, embedding layers. ○ tf.keras: Building and training NLP models. PyTorch Overview What is PyTorch? ○ Open-source deep learning framework. ○ Developed by Facebook AI. Key Features: ○ Dynamic computation graph. ○ Strong GPU acceleration. ○ Pythonic interface. Best for: #install with pip install torch torchvision torchaudio ○ Research-focused NLP projects. #verify installation ○ Custom deep learning models. import torch print(torch.__version__) PyTorch in NLP Applications: ○ Recurrent Neural Networks RNNs, Transformers. ○ Sequence-to-sequence models for machine translation. Popular Libraries: ○ torchtext: Preprocessing and datasets. ○ Hugging Face Transformers PyTorch backend). Hugging Face Overview What is Hugging Face? ○ A library for NLP with pre-trained models. ○ Built on PyTorch and TensorFlow. Key Features: ○ Pre-trained Transformers (e.g., BERT, GPT. ○ Easy-to-use APIs. ○ Tokenizers and datasets. #install with Best for: pip install transformers pip install datasets ○ Leveraging state-of-the-art models. #verify installation ○ Rapid prototyping and transfer learning. from transformers import pipeline from datasets import Dataset Hugging Face in NLP Applications: ○ Sentiment analysis, text summarization, question answering, and more. Tools: ○ transformers: Models and tokenizers. ○ datasets: Easy access to NLP datasets. Transformers A Transformer is a type of deep learning model designed to understand and generate text by analyzing the relationships between words in a sentence, no matter where they appear. It uses a mechanism called attention to focus on the most important parts of the input, enabling it to perform tasks like translation, summarization, and sentiment analysis very efficiently. Comparison of Tools Summary Key Takeaways: ○ Scikit-learn: Ideal for classical ML. ○ PyTorch: Flexible and research-friendly deep learning. ○ TensorFlow: Scalable and production-ready. ○ Hugging Face: Easy access to state-of-the-art NLP models. Next Steps: ○ Experiment with these tools for your own NLP projects. ○ Explore additional resources and documentation. Thank you