Coming up to speed on NLP using HuggingFace

Created: by Pradeep Gowda — Updated:Aug 20, 2024 — Tagged: nlp · huggingface · claude .

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

Introduction to Neuro Linguistic Programming (NLP)

Applications of NLP:

The input for NLP is text - unstructured data.

Methods of processing data for NLP include:

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

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:
  1. Benefits:
  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

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)