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Coming up to speed on NLP using HuggingFace

7 min read

ATTENTION CONSERVATION NOTICE: This self-study guide was prepared with the help of Claude.

Introduction to Neuro Linguistic Programming (NLP)

Applications of NLP:

  • language translation
  • grammatical error correction
  • sentiment analysis
  • fake news detection

The input for NLP is text - unstructured data.

Methods of processing data for NLP include:

  • Tokenization — break raw text into smaller chunks (could be words or sentences).
    • Libraries for Tokenization are:
      • NLTK
      • Gensim
      • Keras
    • Methods of tokenization are:
      • white space tokenization
      • rule based tokenization
      • penn tree tokenization
      • dictionary based tokenization
      • spacy tokenization
      • moses tokenization
      • subword tokenization
        • Byte pair encoding
        • word piece encoding
        • Sentence piece encoding
        • Unigram language model
  • Stop word removal — stop words do not add any meaning to the sentence, and can be removed.
  • Stemming
  • Normalization
  • Lemmatization
  • Parts of speech tagging

NLP Basics and Hugging Face Introduction

Overview of NLP concepts (tokenization, embeddings, etc.)

  1. Tokenization:
  2. Embeddings:
  3. General NLP Concepts:
  4. Hugging Face-specific NLP concepts:

Introduction to Hugging Face and its ecosystem

Transformers Library

  1. Official Documentation:
  2. Tutorials and Guides:
  3. Video Tutorials:
  4. Practical Guides:
  5. Key Classes and Methods:
  6. Hands-on Practice:

Explore the Transformers library, focusing on key classes and methods

For your 60-minute deep dive, I’d recommend:

  1. Start with the “Introduction to Transformers” from the Hugging Face course (15 minutes)
  2. Watch the first 20-30 minutes of the “Hugging Face Transformers Library - Full Course” video
  3. Spend the remaining time exploring the “Main Classes” section of the official documentation and try running a few examples from the Notebooks repository

Practical NLP Tasks

Text classification with pre-trained models

Named Entity Recognition (NER) using Hugging Face

  1. What is NER? NER is the task of identifying and classifying named entities (like persons, organizations, locations) in text.
  2. Hugging Face and NER: Hugging Face offers pre-trained models and easy-to-use pipelines for NER tasks.
  3. Key Components: a) Pipeline API: The simplest way to perform NER with Hugging Face. b) Pre-trained Models: Models like BERT, RoBERTa, and others fine-tuned for NER. c) Tokenizers: Essential for preprocessing text for NER tasks.
  4. Basic Usage: Here’s a simple example of using Hugging Face for NER:
from transformers import pipeline
 
ner = pipeline("ner")
text = "Apple Inc. is headquartered in Cupertino, California."
result = ner(text)
print(result)
  1. Fine-tuning: You can fine-tune pre-trained models on your specific NER dataset for better performance.
  2. Resources:

Advanced Topics and Hands-on Practice

Fine-tuning pre-trained models

  • Fine-tuning Concept: Fine-tuning adapts a pre-trained model to a specific task or domain by training it further on a smaller, task-specific dataset.

  • Custom Datasets: These are datasets you create or obtain that are specific to your NER task or domain.

  • Process Overview: a) Prepare your dataset:

    • Format your data (typically in CoNLL format for NER)
    • Split into train/validation/test sets

    b) Load a pre-trained model:

    • Choose a model (e.g., BERT, RoBERTa) from Hugging Face

    c) Set up fine-tuning:

    • Configure training arguments
    • Create a trainer

    d) Train the model:

    • Run the fine-tuning process
    • Monitor metricse) Evaluate and use the model

  • Code Example: Here’s a simplified example of fine-tuning a model for NER:

from transformers import AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer
from datasets import load_dataset
 
# Load dataset
dataset = load_dataset("your_custom_dataset")
 
# Load pre-trained model and tokenizer
model = AutoModelForTokenClassification.from_pretrained("bert-base-cased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
 
# Tokenize dataset
def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
    labels = []
    for i, label in enumerate(examples["ner_tags"]):
        word_ids = tokenized_inputs.word_ids(batch_index=i)
        previous_word_idx = None
        label_ids = []
        for word_idx in word_ids:
            if word_idx is None:
                label_ids.append(-100)
            elif word_idx != previous_word_idx:
                label_ids.append(label[word_idx])
            else:
                label_ids.append(-100)
            previous_word_idx = word_idx
        labels.append(label_ids)
    tokenized_inputs["labels"] = labels
    return tokenized_inputs
 
tokenized_dataset = dataset.map(tokenize_and_align_labels, batched=True)
 
# Set up training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
)
 
# Create Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["validation"],
    tokenizer=tokenizer,
)
 
# Fine-tune the model
trainer.train()
  1. Challenges and Considerations:
  • Data quality and quantity are crucial
  • Balancing classes in your dataset
  • Overfitting on small datasets
  • Computational resources required
  1. Benefits:
  • Improved performance on domain-specific tasks
  • Ability to recognize custom entity types
  1. Resources:

Exploring the Model Hub and implementing a chosen NLP task

The Hugging Face Model Hub is a central repository for pre-trained models, and it’s an excellent resource for implementing various NLP tasks. Here’s an overview of exploring the Model Hub and implementing an NLP task:

  1. The Model Hub:
    • A platform hosting thousands of pre-trained models
    • Covers various NLP tasks, including translation, summarization, question answering, etc.
    • URL: https://huggingface.co/models
  2. Exploring the Model Hub:
    • Use filters to narrow down models by task, language, framework, etc.
    • Each model has a dedicated page with description, usage examples, and performance metrics
    • You can directly use models or download them for local use
  3. Implementing an NLP Task: Let’s walk through the process of implementing a task using a model from the Hub. We’ll use text summarization as an example.

Step 1: Find a suitable model

  • Go to the Model Hub and filter for “summarization” task
  • Choose a model, e.g., “facebook/bart-large-cnn”

Step 2: Load the model and tokenizer

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
model_name = "facebook/bart-large-cnn"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

Step 3: Prepare your input text

text = """ Your long text to be summarized goes here. It can be multiple sentences or paragraphs long. """

Step 4: Tokenize the input and generate the summary

inputs = tokenizer([text], max_length=1024, return_tensors="pt", truncation=True)
summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)

Step 5: Decode and print the summary

summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) print(summary)
  • Other Popular NLP Tasks:
    • Text Classification
    • Named Entity Recognition
    • Question Answering
    • Machine Translation
    • Sentiment Analysis
  • Best Practices:
    • Read the model card for usage instructions and limitations
    • Check the license for commercial use if applicable
    • Consider fine-tuning for domain-specific tasks
    • Be aware of potential biases in pre-trained models
  • Community Aspect:
    • You can contribute your own models to the Hub
    • Collaborate with others and build on existing work
  • Resources:

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