Gegenereerde tekst classificeren voor RLHF

Je wilt de gegenereerde reviews nu categoriseren. Een van de manieren om de output te evalueren is door de positiviteit van de gegenereerde reviews te meten met de classifier lvwerra/distilbert-imdb, die je ook kunt initialiseren via Hugging Face-pipelines.

De pipeline-bibliotheek is al vooraf geïmporteerd uit transformers. Het lvwerra/distilbert-imdb-model is al vooraf geladen als model. De tokenizer is al vooraf geladen als tokenizer.

Deze oefening maakt deel uit van de cursus

Reinforcement Learning from Human Feedback (RLHF)

Cursus bekijken

Oefeninstructies

Gebruik de functie pipeline om een sentimentanalyse-pipeline met het model te maken.
Classificeer het sentiment van de gegeven review.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create a sentiment analysis pipeline
sentiment_analyzer = pipeline(____, model=____, tokenizer=____)

review_text = "Surprisingly, the film is a very good one"

# Classify the sentiment of the review
sentiment = sentiment_analyzer(____)
print(f"Sentiment Analysis Result: {sentiment}")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Reinforcement Learning from Human Feedback (RLHF)

SkillTag.level.advancedSkillTag.label

4.8+

Begin de cursus gratis

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: Introductie tot RLHF Exercise 2: Tekstgeneratie met RLHF Exercise 3: Gegenereerde tekst classificeren voor RLHF

Huidige oefening

Exercise 4: RL vs. RLHF Exercise 5: Voorgetrainde LLM's verkennen Exercise 6: Tokenizeer een tekstgegevensset Exercise 7: Fine-tunen voor reviewclassificatie Exercise 8: Data voorbereiden voor RLHF Exercise 9: De voorkeursgegevensset voorbereiden Exercise 10: Prompts extraheren

Discover how to set up systems for gathering human feedback in this Chapter. Learn best practices for collecting high-quality data, from pairwise comparisons to uncertainty sampling, and explore strategies for enhancing your data collection.

Exercise 1: Methods for high-quality feedback gathering Exercise 2: Understanding comparison and rating in RLHF Exercise 3: Comparing slogans for a gym campaign Exercise 4: Measuring feedback quality and relevance Exercise 5: Low confidence Exercise 6: K-means for feedback clustering Exercise 7: Active learning Exercise 8: Implementing an active learning pipeline Exercise 9: Active learning loop

In this Chapter, you'll get into the core of Reinforcement Learning from Human Feedback training. This includes exploring fine-tuning with PPO, techniques to train efficiently, and handling potential divergences from your metrics' objectives.

Exercise 1: Reward models explored Exercise 2: Initializing the reward Exercise 3: Setting up the reward trainer Exercise 4: Training with PPO Exercise 5: Initialize the PPO trainer Exercise 6: PPO fine-tuning Exercise 7: Efficient fine-tuning in RLHF Exercise 8: Prepare for 8-bit Training Exercise 9: Train with LoRA

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.

Exercise 1: Model metrics and adjustments Exercise 2: Mitigating negative KL divergence Exercise 3: Checking the reward model Exercise 4: Incorporating diverse feedback sources Exercise 5: Majority voting on multiple data sources Exercise 6: Unreliable data source identification Exercise 7: Evaluating RLHF models Exercise 8: Interpreting curves Exercise 9: Evaluating RLHF with metrics Exercise 10: Wrapping up your RLHF journey