Tokenize a text dataset

You are working on market research for a travel website, and would like to use an existing dataset to fine tune a model that will help you classify hotel reviews. You decide to use the datasets library.

The AutoTokenizer class has been pre-imported from transformers, and load_dataset() has been pre-imported from datasets.

This exercise is part of the course

Reinforcement Learning from Human Feedback (RLHF)

Exercise instructions

Add padding to the tokenizer to process text as equal-sized batches.
Tokenize the text data using the pre-trained GPT tokenizer and defined function.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

dataset = load_dataset("argilla/tripadvisor-hotel-reviews")

tokenizer = AutoTokenizer.from_pretrained("openai-gpt")

# Add padding with the pad token
tokenizer.____

def tokenize_function(examples):
   return tokenizer(examples["text"], padding="max_length", truncation=True)

# Tokenize the dataset
tokenized_datasets = dataset.map(____, batched=True)

Edit and Run Code

This exercise is part of the course

Reinforcement Learning from Human Feedback (RLHF)

AdvancedSkill Level

4.8+

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This chapter introduces the basics of Reinforcement Learning with Human Feedback (RLHF), a technique that uses human input to help AI models learn more effectively. Get started with RLHF by understanding how it differs from traditional reinforcement learning and why human feedback can enhance AI performance in various domains.

Exercise 1: Introduction to RLHF Exercise 2: Text generation with RLHF Exercise 3: Classifying generated text for RLHF Exercise 4: RL vs. RLHF Exercise 5: Exploring pre-trained LLMs Exercise 6: Tokenize a text dataset

Current Exercise

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Explore key techniques for assessing and improving model performance in this last Chapter of Reinforcement Learning from Human Feedback (RLHF): from fine-tuning metrics to incorporating diverse feedback sources, you'll be provided with a comprehensive toolkit to refine your models effectively.

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